Systems and methods relating to customer experience automation

ABSTRACT

A computer-implemented method for automating actions for a customer in relation to an interaction between the customer and an agent of a contact center, the interaction including an exchange of statements made by the customer and agent. The method includes the steps of: receiving a transcript of the interaction; via a first analysis, analyzing the transcript; from results of the first analysis, identifying: a pending action, wherein the pending action is an action promised by the customer or agent that will be resolved after the interaction; and a target timeframe for resolving the pending action; given the pending action, determining a follow-up workflow that includes one or more follow-up actions, each of the one or more follow-up actions comprising an action intended to assist the customer to resolve the pending action; and automatically executing the one or more follow-up actions.

BACKGROUND

The present invention generally relates to telecommunications systems inthe field of customer relations management including customer assistancevia internet-based service options. More particularly, but not by way oflimitation, the present invention pertains to systems and methods forautomating the customer experience, including aspects of customerservice offered through an application executed on a mobile computingdevice.

BRIEF DESCRIPTION OF THE INVENTION

The present invention may include a computer-implemented method forautomating actions for a customer in relation to an interaction betweenthe customer and an agent of a contact center, wherein the interactionincludes an exchange of statements made by the customer and the agent.The method may include: receiving at least a transcript of theinteraction; via a first analysis, analyzing the transcript of theinteraction; from results of the first analysis, identifying: a pendingaction, wherein the pending action comprises an action promised by thecustomer or the agent that will be resolved after the interaction; and atarget timeframe for resolving the pending action; given the pendingaction, determining a follow-up workflow that includes one or morefollow-up actions, each of the one or more follow-up actions comprisingan action intended to assist the customer to resolve the pending action;and automatically executing the one or more follow-up actions.

The present invention may include a system for automating actions for acustomer in relation to an interaction between the customer and an agentof a contact center, wherein the interaction includes an exchange ofstatements made by the customer and statements made by the agent. Thesystem may include: a hardware processor; and a machine-readable storagemedium on which is stored instructions that cause the hardware processorto execute a process. The process may include the steps of: receiving atleast a transcript of the interaction; via a first analysis, analyzingthe transcript of the interaction; from results of the first analysis,identifying: a pending action, wherein the pending action comprises anaction promised by the customer or the agent that will be resolved afterthe interaction; and a target timeframe for resolving the pendingaction; given the pending action, determining a follow-up workflow thatincludes one or more follow-up actions, each of the one or morefollow-up actions comprising an action intended to assist the customerto resolve the pending action; and automatically executing the one ormore follow-up actions.

These and other features of the present application will become moreapparent upon review of the following detailed description of theexample embodiments when taken in conjunction with the drawings and theappended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the present invention, and many of theattendant features and aspects thereof, will become more readilyapparent as the invention becomes better understood by reference to thefollowing detailed description when considered in conjunction with theaccompanying drawings in which like reference symbols indicate likecomponents, wherein:

FIG. 1 depicts a schematic block diagram of a computing device inaccordance with exemplary embodiments of the present invention and/orwith which exemplary embodiments of the present invention may be enabledor practiced;

FIG. 2 depicts a schematic block diagram of a communicationsinfrastructure or contact center in accordance with exemplaryembodiments of the present invention and/or with which exemplaryembodiments of the present invention may be enabled or practiced;

FIG. 3 is schematic block diagram showing further details of a chatserver operating as part of the chat system according to embodiments ofthe present invention;

FIG. 4 is a schematic block diagram of a chat module according toembodiments of the present invention;

FIG. 5 is an exemplary customer chat interface according to embodimentsof the present invention;

FIG. 6 is a block diagram of a customer automation system according toembodiments of the present invention;

FIG. 7 is a flowchart of a method for automating an interaction onbehalf of a customer according to embodiments of the present invention;

FIG. 8 is a block diagram of an automated personal bot for a customeraccording to embodiments of the present invention;

FIG. 9 is an example as to how a customer interaction is processedaccording to embodiments of the present invention;

FIG. 10 is an example as to how a customer interaction is processedaccording to embodiments of the present invention; and

FIG. 11 is an example as to how a customer interaction is processedaccording to embodiments of the present invention.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of theinvention, reference will now be made to the exemplary embodimentsillustrated in the drawings and specific language will be used todescribe the same. It will be apparent, however, to one having ordinaryskill in the art that the detailed material provided in the examples maynot be needed to practice the present invention. In other instances,well-known materials or methods have not been described in detail inorder to avoid obscuring the present invention. Additionally, furthermodification in the provided examples or application of the principlesof the invention, as presented herein, are contemplated as wouldnormally occur to those skilled in the art.

As used herein, language designating nonlimiting examples andillustrations includes “e.g.”, “i.e.”, “for example”, “for instance” andthe like. Further, reference throughout this specification to “anembodiment”, “one embodiment”, “present embodiments”, “exemplaryembodiments”, “certain embodiments” and the like means that a particularfeature, structure or characteristic described in connection with thegiven example may be included in at least one embodiment of the presentinvention. Thus, appearances of the phrases “an embodiment”, “oneembodiment”, “present embodiments”, “exemplary embodiments”, “certainembodiments” and the like are not necessarily all referring to the sameembodiment or example. Further, particular features, structures orcharacteristics may be combined in any suitable combinations and/orsub-combinations in one or more embodiments or examples.

Embodiments of the present invention may be implemented as an apparatus,method, or computer program product. Accordingly, example embodimentsmay take the form of an entirely hardware embodiment, an entirelysoftware embodiment (including firmware, resident software, micro-code,etc.), or an embodiment combining software and hardware aspects that mayall generally be referred to herein as a “module” or “system.” Further,example embodiments may take the form of a computer program productembodied in any tangible medium of expression having computer-usableprogram code embodied in the medium. In addition, it will be appreciatedthat the figures provided herewith are for explanation purposes topersons ordinarily skilled in the art and that the drawings are notnecessarily drawn to scale.

It will be further appreciated that the flowchart and block diagramsprovided in the figures illustrate architecture, functionality, andoperation of possible implementations of systems, methods, and computerprogram products according to example embodiments of the presentinvention. In this regard, each block in the flowchart or block diagramsmay represent a module, segment, or portion of code, which comprises oneor more executable instructions for implementing the specified logicalfunctions. It will also be noted that each block of the block diagramsand/or flowchart illustrations, and combinations of blocks in the blockdiagrams and/or flowchart illustrations, may be implemented by specialpurpose hardware-based systems that perform the specified functions oracts, or combinations of special purpose hardware and computerinstructions. These computer program instructions may also be stored ina computer-readable medium that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

Exemplary Computing Device

Turning now to FIG. 1, a schematic block diagram of an exemplarycomputing device 100 is shown in accordance with embodiments of thepresent invention and/or with which exemplary embodiments of the presentinvention may be enabled or practiced. It should be appreciated thatFIG. 1 is provided as a non-limiting example.

The computing device 100, as used herein, may be implemented viafirmware (e.g., an application-specific integrated circuit), hardware,or a combination of software, firmware, and hardware. It will beappreciated that each of the servers, controllers, switches, gateways,engines, and/or modules in the following figures (which collectively maybe referred to as servers) may be implemented via one or more of thecomputing devices 100. For example, the various servers may be a processor thread running on one or more processors of one or more computingdevices 100 executing computer program instructions and interacting withother system components for performing the various functionalitiesdescribed herein. A server also may be a software module, for example, asoftware module of the contact center 200 depicted in FIG. 2 may includeone or more servers. Further, unless otherwise specifically limited, thefunctionality described in relation to a plurality of computing devicesmay be integrated into a single computing device 100, or thefunctionality described in relation to a single computing device may bedistributed across several computing devices 100. In relation tocomputing systems described herein, such as the contact center 200 ofFIG. 2, the various servers and computer systems thereof may be locatedon one or more local computing devices 100 (i.e., on-site at the samephysical location as the agents of the contact center) or may be locatedon one or more remote computing devices 100 (i.e., off-site or in thecloud in a geographically different location, for example, in a remotedata center connected to the contact center via a network). In exemplaryembodiments, functionality provided by servers located on computingdevices off-site may be accessed and provided over a virtual privatenetwork (VPN) as if such servers were on-site, or the functionality maybe provided using a software as a service (SaaS) to providefunctionality over the Internet using various protocols, such as byexchanging data using encoded in extensible markup language (XML) orJSON.

Though other configurations are also possible, in the illustratedexample, the computing device 100 include a central processing unit(CPU) or processor 105 and a main memory 110. The computing device 100also includes a storage device 115, a removable media interface 120, anetwork interface 115, one or more input/output (I/O) devices 135, whichas depicted includes an I/O controller 130, a display device 135A, akeyboard 135B, and a pointing device 135C (e.g., a mouse). The storagedevice 115 may provide storage for an operating system and software runon the computing device. The computing device 100 further includesadditional optional elements, such as a memory port 140, a bridge 145,one or more additional input/output devices 135D, 135E, 135F, and acache memory 150 in communication with the processor 105.

The processor 105 of the computing device 100 may be any logic circuitrythat responds to and processes instructions fetched from the main memory110. It may be implemented, for example, in an integrated circuit, inthe form of a microprocessor, microcontroller, or graphics processingunit, or in a field-programmable gate array (FPGA) orapplication-specific integrated circuit (ASIC). The main memory 110 maybe one or more memory chips capable of storing data and allowing anystorage location to be directly accessed by the central processing unit105. Though other configurations are possible, as shown in theillustrated example, the central processing unit 105 may communicatedirectly with the main memory 110 via a memory port 140 and indirectlywith the storage device 115 via a system bus 155.

In exemplary embodiments, the processor 105 may include a plurality ofprocessors and may provide functionality for simultaneous execution ofinstructions or for simultaneous execution of one instruction on morethan one piece of data. The computing device 100 may include a parallelprocessor with one or more cores. The computing device 100 may include ashared memory parallel device, with multiple processors and/or multipleprocessor cores, accessing all available memory as a single globaladdress space. In another embodiment, the computing device 100 may be adistributed memory parallel device with multiple processors eachaccessing local memory only. The computing device 100 may have both somememory which is shared and some which may only be accessed by particularprocessors. The processor 105 may include a multicore microprocessor,which combines two or more independent processors into a single package,e.g., into a single integrated circuit (IC). In exemplary embodiments,the processor 105 may provide single instruction multiple data (SIMD)functionality. In another embodiment, several processors in theprocessor 105 may provide functionality for execution of multipleinstructions simultaneously on multiple pieces of data (MIMD).

As depicted in FIG. 1, the processor 105 may communicate directly withthe cache memory 150 via a secondary bus or backside bus. In otherembodiments, the processor 105 communicates with the cache memory 150using the system bus 155. The cache memory 150 typically has a fasterresponse time than main memory 110. As illustrated, the processor 105may communicate with various I/O devices 135 via the local system bus155, though direct communication though backside buses are alsopossible. Various buses may be used as the local system bus 155 inaccordance with conventional technology. For embodiments in which an I/Odevice is a display device 135A, the processor 105 may communicate withthe display device 135A through an advanced graphics port (AGP).

A wide variety of I/O devices 135 may be present in the computing device100. Input devices may include one or more keyboards 135, mice,trackpads, trackballs, microphones, and drawing tablets, to name a fewnon-limiting examples. Output devices may include video display devices,speakers and printers. An I/O controller 130 may be used to control theI/O devices, such as, for example, as a keyboard 135B and a pointingdevice 135C (e.g., a mouse or optical pen).

The computing device 100 may support one or more removable mediainterfaces 120, such as a floppy disk drive, a CD-ROM drive, a DVD-ROMdrive, tape drives of various formats, a USB port, or any other devicesuitable for reading data from read-only media, or for reading datafrom, or writing data to, read-write media. The removable mediainterface 120, for example, may be used for installing software andprograms. The computing device 100 may further include a storage device115, such as one or more hard disk drives or hard disk drive arrays, forstoring an operating system and other related software. Optionally, aremovable media interface 120 may also be used as the storage device.

The computing device 100 may include or be connected to multiple displaydevices 135A. As such, any of the I/O devices 135 and/or the I/Ocontroller 130 may include any type and/or form of suitable hardware,software, or combination of hardware and software to support, enable orprovide for the connection to, and use of, the multiple display devices135A by the computing device 100. For example, the computing device 100may include any type and/or form of video adapter, video card, driver,and/or library to interface, communicate, connect or otherwise use themultiple display devices 135A. In exemplary embodiments, a video adaptermay include multiple connectors to interface to multiple display devices135A. In another embodiment, the computing device 100 may includemultiple video adapters, with each video adapter connected to one ormore of the display devices 135A. In other embodiments, one or more ofthe display devices 135A may be provided by one or more other computingdevices, connected, for example, to the computing device 100 via anetwork. These embodiments may include any type of software designed andconstructed to use the display device of another computing device as asecond display device 135A for the computing device 100. One of ordinaryskill in the art will recognize and appreciate the various ways andembodiments that a computing device 100 may be configured to havemultiple display devices 135A.

The computing device 100 may operate under the control of an operatingsystem, which controls scheduling of tasks and access to systemresources. The computing device 100 may run any operating system,embedded operating system, real-time operating system, open sourceoperation system, proprietary operating system, mobile device operatingsystem, or any other operating system capable of running on a computingdevice and performing the operations described herein. The computingdevice 100 may be any workstation, desktop computer, laptop or notebookcomputer, server machine, handled computer, mobile telephone, smartphone, portable telecommunication device, media playing device, gamingsystem, mobile computing device, or any other type and/or form ofcomputing, telecommunications or media device that is capable ofcommunication and that has sufficient processor power and memorycapacity to perform the operations described herein. In exemplaryembodiments, the computing device 100 may have different processors,operating systems, and input devices consistent with the device. Incertain embodiments, the computing device 100 is a mobile device. Inexemplary embodiments, the computing device 100 may include acombination of devices, such as a mobile phone combined with a digitalaudio player or portable media player.

The computing device 100 may be one of a plurality of machines connectedby a network, or it may include a plurality of machines so connected. Anetwork environment may include one or more local machine(s), client(s),client node(s), client machine(s), client computer(s), client device(s),endpoint(s), or endpoint node(s) in communication with one or moreremote machines (which may also be generally referred to as servermachines or remote machines) via one or more networks. In exemplaryembodiments, a local machine has the capacity to function as both aclient node seeking access to resources provided by a server machine andas a server machine providing access to hosted resources for otherclients. The network may be LAN or WAN links, broadband connections,wireless connections, or a combination of any or all of the above.Connections may be established using a variety of communicationprotocols. In one embodiment, the computing device 100 communicates withother computing devices 100 via any type and/or form of gateway ortunneling protocol such as secure socket layer (SSL) or transport layersecurity (TLS). The network interface may include a built-in networkadapter, such as a network interface card, suitable for interfacing thecomputing device to any type of network capable of communication andperforming the operations described herein. As discussed more below,aspects of the computing device 100 may include components, serves, orother modules that are a cloud-based or implemented within a cloudcomputing environment

In exemplary embodiments, a network environment may be a virtual networkenvironment where the various network components are virtualized. Forexample, the various machines may be virtual machines implemented as asoftware-based computer running on a physical machine. The virtualmachines may share the same operating system or, in other embodiments,different operating system may be run on each virtual machine instance.In exemplary embodiments, a “hypervisor” type of virtualizing is usedwhere multiple virtual machines run on the same host physical machine,each acting as if it has its own dedicated box. The virtual machines mayalso run on different host physical machines. Other types ofvirtualization are also contemplated, such as, for example, the network(e.g., via software defined networking (SDN)). Functions, such asfunctions of a session border controller, may also be virtualized, suchas, for example, via network functions virtualization (NFV).

Contact Centers

With reference now to FIG. 2, a communications infrastructure orcustomer service contact center (hereinafter “contact center”) 200 isshown in accordance with exemplary embodiments of the present inventionand/or with which exemplary embodiments of the present invention may beenabled or practiced.

By way of background, customer service providers generally offer manytypes of services through contact centers. Such contact centers may bestaffed with employees and/or customer service agents (or simply“agents”), with the agents serving as an interface between anorganization, such as a company, enterprise, or government agency, andpersons, such as users or customers (hereinafter generally referred toas “customers”). For example, the agents at a contact center may assistcustomers in making purchasing decisions and receive purchase orders.Similarly, agents may assist or support customers in solving problemswith products or services already provided by the organization. Within acontact center, such interactions between contact center agents andoutside entities or customers may be conducted over a variety ofcommunication channels, such as, for example, via voice (e.g., telephonecalls or voice over IP or VoIP calls), video (e.g., video conferencing),text (e.g., emails and text chat), or through other media.

Operationally, contact centers generally strive to provide qualityservices to customers, while minimizing costs. For example, one way fora contact center to operate is to handle every customer interaction witha live agent. While this approach may score well in terms of the servicequality, it likely would also be prohibitively expensive due to the highcost of agent labor. Because of this, most contact centers utilize somelevel of automated processes in place of live agents, such as, forexample, interactive voice response (IVR) systems, interactive mediaresponse (IMR) systems, internet robots or “bots”, automated chatmodules or “chatbots”, and the like. In many cases this has proven to bea successful strategy, as automated processes can be highly efficient inhandling certain types of interactions and effective at decreasing theneed for live agents. Such automation allows contact centers to targetthe use of human agents for the more difficult customer interactions,while the automated processes handle the more repetitive or routinetasks. Further, automated processes can be structured in a way thatoptimizes efficiency and promotes repeatability. Whereas a human or liveagent may forget to ask certain questions or follow-up on particulardetails, such mistakes are typically avoided through the use ofautomated processes. As a result, customer service providers areincreasingly relying on automated processes to interact with customers.

However, while such automation technology is now commonly used bycontact centers to increase efficiency, it remains far less developedfor use by customers. Thus, while IVR systems, IMR systems, and/or botsare used to automate portions of the interaction on the contact centerside of the interaction, the actions on the customer-side are still leftfor the customer to perform manually. As will be seen, embodiments ofthe present invention relate to systems and methods for automatingaspects of the customer-side of the interactions between customers andcustomer service providers or contact centers. Accordingly, presentembodiments may provide ways to automate actions that customers arerequired to perform when contacting and interacting with customerservice providers or contact centers. For example, embodiments of thepresent invention include methods and systems for identifyingoutstanding matters or pending actions for a customer that needadditional attention stemming from a previous interaction between thecustomer and a contact center. Once identified, other embodiments mayinclude methods and systems for automating follow-up actions on behalfof the customer for resolving such pending actions.

Referring specifically to FIG. 2, a block diagram is presented thatillustrates an embodiment of a communication infrastructure or contactcenter 200 in accordance with the present invention and/or anenvironment within which embodiments of the present invention may beenabled or practiced. The contact center 200 may be used by a customerservice provider to provide various types of services to customers. Forexample, the contact center 200 may be used to engage and manage chatconversations in which automated chat robots or bots and/or human agentscommunicate with customers. As will be appreciated, the contact center200 may be used as an in-house facility to a business or enterprise forserving the enterprise in performing the functions of sales and servicerelative to the products and services available through the enterprise.In another aspect, the contact center 200 may be operated by athird-party service provider. According to another embodiment, thecontact center 200 may operate as a hybrid system in which somecomponents are hosted at the contact center premise while othercomponents are hosted remotely (e.g., in a cloud-based or cloudcomputing environment). The contact center 200 may be deployed onequipment dedicated to the enterprise or third-party service provider,and/or deployed in a remote computing environment such as, for example,a private or public cloud environment with infrastructure for supportingmultiple contact centers for multiple enterprises. As discussed morebelow, the contact center 200 may include software applications orprograms, which may be executed on premises or remotely or somecombination thereof. It should further be appreciated that the variouscomponents of the contact center 200 may also be distributed acrossvarious geographic locations and computing environments and notnecessarily contained in a single location, computing environment, oreven computing device.

Further, it should be generally noted that, unless otherwisespecifically limited, any of the computing elements of present inventionmay be implemented in cloud-based or cloud computing environments. Asused herein, “cloud computing” may be defined as a model for enablingubiquitous, convenient, on-demand network access to a shared pool ofconfigurable computing resources (e.g., networks, servers, storage,applications, and services) that can be rapidly provisioned viavirtualization and released with minimal management effort or serviceprovider interaction, and then scaled accordingly. Cloud computing canbe composed of various characteristics (e.g., on-demand self-service,broad network access, resource pooling, rapid elasticity, measuredservice, etc.), service models (e.g., Software as a Service (“SaaS”),Platform as a Service (“PaaS”), Infrastructure as a Service (“IaaS”),and deployment models (e.g., private cloud, community cloud, publiccloud, hybrid cloud, etc.). Also often referred to as a “serverlessarchitecture”, a cloud computing (or simply “cloud”) execution modelgenerally includes a service provider dynamically managing an allocationand provisioning of remote servers for achieving a desiredfunctionality. It will be appreciated that such “serverless” platformsstill require servers.

In accordance with the exemplary embodiment of FIG. 2, the components ormodules of the contact center 200 may include: a plurality of customerdevices 205A, 205B, 205C; a communications network 210 (also referred tosimply as network 210); a switch/media gateway 212; a call controller214; an interactive media response (IMR) server 216; a routing server218; a storage device 220; a statistics or stat server 226; a pluralityof agent devices 230A, 230B, 230C that include workbins 232A, 232B,232C, respectively; a multimedia/social media server 234; a knowledgemanagement server 234 coupled to a knowledge system 238; a chat server240, web servers 242; an interaction (iXn) server 244; a universalcontact server (UCS) 246; a reporting server 248; media services 249;and an analytics module 250. As will be seen, the contact center 200manages resources (e.g., personnel, computers, telecommunicationequipment, etc.) to enable delivery of services via telephone, email,chat, or other communication mechanisms. Such services may varydepending on the type of contact center and range from customer serviceto help desk, emergency response, telemarketing, order taking, etc.

For example, in accordance with an embodiment, customers desiring toreceive services from the contact center 200 may initiate inboundcommunications (e.g., telephone calls, emails, chats, etc.) to thecontact center 200 via a customer device 205. While FIG. 2 shows threesuch customer devices—i.e., customer devices 205A, 205B, and 205C—itshould be understood that any number may be present. Each of thecustomer devices 205 may be a communication device conventional in theart, such as a telephone, wireless phone, smart phone, personalcomputer, electronic tablet, or laptop, to name some non-limitingexamples. In general, the customer devices 205 are used by customers toinitiate, manage, and respond to telephone calls, emails, chats, textmessages, web-browsing sessions, and other multi-media transactions inaccordance with any of the functionality described herein. For example,a customer may use a customer device 205 to contact the contact center200 by way of a chat channel with the text being transmitted to achatbot or human agent. A response from the chatbot or human agent maybe generated and delivered to the customer device 205 as text.

Inbound and outbound communications from and to the customer devices 205may traverse the network 210, with the nature of network depending onthe type of customer device being used and form of communication. As anexample, the network 210 may include a communication network oftelephone, cellular, and/or data services and may also comprise aprivate or public switched telephone network (PSTN), local area network(LAN), private wide area network (WAN), and/or public WAN such as theInternet. The network 210 may also include a wireless carrier networkincluding a code division multiple access (CDMA) network, global systemfor mobile communications (GSM) network, or any wirelessnetwork/technology conventional in the art, including but not limited to3G, 4G, LTE, etc.

Embodiments of the contact center 200 may include a switch/media gateway212 coupled to the network 210 for receiving and transmitting telephonecalls between the customers and the contact center 200. The switch/mediagateway 212 may include a telephone switch or communication switchconfigured to function as a central switch for agent level routingwithin the center. The switch may be a hardware switching system or asoft switch implemented via software. For example, the switch 215 mayinclude an automatic call distributor, a private branch exchange (PBX),an IP-based software switch, and/or any other switch with specializedhardware and software configured to receive Internet-sourcedinteractions and/or telephone network-sourced interactions from acustomer, and route those interactions to, for example, an agenttelephone or communication device. In this example, the switch/mediagateway establishes a voice path/connection between the calling customerand the agent telephone device, by establishing, for example, aconnection between the customer's telephone device and the agenttelephone device.

In exemplary embodiments, the switch is coupled to a call controller 214which, for example, serves as an adapter or interface between the switchand the remainder of the routing, monitoring, and othercommunication-handling components of the contact center. The callcontroller 214 may be configured to process PSTN calls, VoIP calls, etc.For example, the call controller 214 may include computer-telephoneintegration (CTI) software for interfacing with the switch/media gatewayand contact center equipment. In exemplary embodiments, the callcontroller 214 may include a session initiation protocol (SIP) serverfor processing SIP calls. The call controller 214 may also extract dataabout the customer interaction, such as the caller's telephone number(e.g., the automatic number identification (ANI) number), the customer'sinternet protocol (IP) address, or email address, and communicate withother components of the contact center 200 in processing theinteraction.

Embodiments of the contact center 200 may include an interactive mediaresponse (IMR) server 216. The IMR server 216 may also be referred to asa self-help system, a virtual assistant, etc. The IMR server 216 may besimilar to an interactive voice response (IVR) server, except that theIMR server 216 is not restricted to voice and additionally may cover avariety of media channels. In an example illustrating voice, the IMRserver 216 may be configured with an IMR script for querying customerson their needs. For example, a contact center for a bank may tellcustomers via the IMR script to ‘press 1’ if they wish to retrieve theiraccount balance. Through continued interaction with the IMR server 216,customers may be able to complete service without needing to speak withan agent. The IMR server 216 may also ask an open-ended question suchas, “How can I help you?” and the customer may speak or otherwise entera reason for contacting the contact center. The customer's response maybe used by a routing server 218 to route the call or communication to anappropriate contact center 200 resource.

For example, if the communication is to be routed to an agent, the callcontroller 214 may interact with the routing server (also referred to asan orchestration server) 218 to find an appropriate agent for processingthe interaction with the particular customer. The selection of anappropriate agent for routing an inbound customer interaction may bebased, for example, on a routing strategy employed by the routing server218, and further based on stored information about the customer andagents (which, as described more below, may be maintained in customerand agent databases on the storage device 220) and other routingparameters provided, for example, by the statistics server 226, whichaggregates data relating to the performance of the contact center 200.The routing server 218, for example, may query such data via an ANI.Thus, in general, the routing server 218 may query data relevant to anincoming interaction for facilitating the routing of that interaction tothe most appropriate contact center.

Regarding data storage, the contact center 200 may include one or moremass storage devices—represented generally by the storage device220—that stores one or more databases of data deemed relevant to thefunctioning of the contact center 200. For example, the storage device220 may store customer data that is maintained in a customer database(also CDB) 222. Customer data maintained by the contact center 200 mayinclude customer profiles, contact information, service level agreement(SLA), and interaction history (e.g., details of each previousinteraction with a customer, including nature of previous customercontacts, reason for the interaction, disposition data, wait time,handle time, and actions taken by the contact center to resolve customerissues). As another example, the storage device 220 may store agent datain an agent database (also ADB) 223. Agent data maintained by thecontact center 200 may include agent availability, profiles, schedules,skills, etc. As another example, the storage device 220 may storeinteraction data in an interaction database (also IDB) 224. Interactiondata may include data relating to numerous past interactions betweencustomers and contact centers. More generally, it should be understoodthat, unless otherwise specified, the storage device 220 is configuredto include databases and/or store data related to any of the types ofinformation described herein, with those databases and/or data beingaccessible to the other modules or servers of the contact center 200 inways that facilitate the functionality described herein. For example,the servers or modules of the contact center 200 may query the databasesfor retrieving particular data stored therewithin as well as transferdata to the databases for storage thereon. The storage device 220, forexample, may take the form of a hard disk, disk array, or any otherstorage medium as is conventional in the art. The storage device 220 maybe included as part of the contact center 200 or operated remotely by athird party. The databases, for example, may be Cassandra or any NoSQLdatabase. The databases may also be a SQL database and be managed by anydatabase management system, such as, for example, Oracle, IBM DB2,Microsoft SQL server, Microsoft Access, PostgreSQL, etc., to name a fewnon-limiting examples.

In exemplary embodiments, the agent devices 230 are configured tointeract with the various components and modules of the contact center200 in ways that facilitate the functionality described herein. Forexample, the agent devices 230 may include a telephone adapted forregular telephone calls, VoIP calls, etc. The agent device 230 mayfurther include a computer for communicating with one or more servers ofthe contact center 200 and performing data processing associated withcontact center operations, as well as for interfacing with customers viavoice and other multimedia communication mechanisms pursuant todescribed functionality. While FIG. 2 shows three such agentdevices—i.e., agent devices 230A, 230B and 230C—it should be understoodthat any number may be present.

Once it is determined that an inbound communication should be handled bya human agent, functionality within the routing server 218 may select anagent from those available for routing the communication thereto. Asalready discussed, this selection may be based on which agent is bestsuited for handling the inbound communication. Once the appropriateagent is selected, the contact center 200 forms a connection between thecustomer device 205 and the agent device 230 that corresponds to theselected agent. As part of this connection, information about thecustomer and/or the customer's history may be provided to the selectedagent via his/her agent device 230. This information generally includesdata that may aid the selected agent to better service the customer.

According to an embodiment, the contact center 200 may include amultimedia/social media server 234 for engaging in media interactionsother than voice interactions with the customer devices 205 and/or webservers 242. The media interactions may be related, for example, toemail, vmail (voice mail through email), chat, video, text-messaging,web, social media, co-browsing, etc. The multi-media/social media server234 may take the form of any IP router conventional in the art withspecialized hardware and software for receiving, processing, andforwarding multi-media events.

Embodiments of the contact center 200 may include a knowledge managementserver 234 for facilitating interactions between customers operating thecustomer devices 205 and a knowledge system 238. The knowledge system238 may be included as part of the contact center 200 or operatedremotely by a third party. In general, the knowledge system 238 may be acomputer system capable of receiving questions or queries and providinganswers in response. The knowledge system 238 may include anartificially intelligent computer system capable of answering questionsposed in natural language by retrieving information from informationsources such as encyclopedias, dictionaries, newswire articles, literaryworks, or other documents submitted to the knowledge system 238 asreference materials, as is known in the art. As an example, theknowledge system 238 may be embodied as IBM Watson®, though other typesof systems also may be used. Additional details of the knowledgemanagement server and knowledge system are provided in U.S. applicationSer. No. 14/449,018, filed on Jul. 31, 2014, entitled “System and Methodfor Controlled Knowledge System Management,” the content of which isincorporated herein by reference.

According to an embodiment, the contact center 200 may include a chatserver 240 for conducting and managing electronic chat communicationswith customers operating customer devices 205. As will be seen, chatcommunications may be conducted by the chat server 240 in such a waythat a customer communicates with both automated systems, which may alsobe referred to as chatbots, as well as human agents, which may also bereferred to simply as agents. According to an embodiment, the chatserver 240 may be configured to implement and maintain chatconversations, generate chat transcripts, and determine whether a chatcommunication is completed (e.g., based on timeout or by a customerclosing a chat window). In exemplary embodiments, the chat server 240may also operate as a chat orchestration server, dispatching actual chatconversations among the chatbots or available human agents. Theprocessing logic of the chat server 240 may be rules driven, andleverage, for example, intelligent workload distribution protocols andvarious business rules for routing communications. The chat server 240further may implement, manage and facilitate user interfaces (also UIs)associated with the chat feature, including those UIs generated ateither the customer device 205 or the agent device 230. Further, thechat server 240 may orchestrate and implement chats conducted by bothhuman agents and automated chatbots. According to an embodiment, thechat server 240 is configured to transfer chats within a single chatsession with a particular customer between automated and human sourcessuch that, for example, a chat session transfers from a chatbot to ahuman agent or from a human agent to a chatbot.

The chat server 240 may also be coupled to the knowledge managementserver 234 and the knowledge systems 238 for receiving suggestions andanswers to queries posed by customers during an automated chat,providing links to knowledge articles, or the like. Additionally, thechat server 240 may be configured to facilitate (e.g., supervise andcoordinate) self-learning by certain of the chatbots. For example, priorto characteristics of individual chatbots being modified, the chatserver 240 may determine whether the feedback from customer thatprecipitated the modification is suspicious or malicious (e.g., bysearching for or identifying key words or phrases, and/or flaggingpotential issues for review by an agent). Although the chat server 240is depicted in the embodiment of FIG. 2 as being a separate servercomponent, a person of skill in the art should recognize thatfunctionalities of the chat server 240 may be incorporated into otherservers, such as, for example, the multimedia/social media server 234 orthe IMR server 216.

According to an embodiment, the web servers 242 may include socialinteraction site hosts for a variety of known social interaction sitesto which a customer may subscribe, such as Facebook, Twitter, Instagram,etc., to name a few non-limiting examples. In exemplary embodiments,although web servers 242 are depicted as part of the contact center 200,the web servers 242 may also be provided by third parties and/ormaintained outside of the contact center premise. The web servers 242may also provide web pages for the enterprise that is being supported bythe contact center 200. Customers may browse the web pages and getinformation about the enterprise's products and services.

The web pages may also provide a mechanism for contacting the contactcenter via, for example, web chat, voice call, email, web real-timecommunication (WebRTC), etc. For example, widgets may be deployed on thewebsites hosted on the web servers 242. As used herein, a widget refersto a user interface component that performs some particular function. Insome implementations, a widget may include a graphical user interfacecontrol that can be overlaid on a web page displayed on the Internet.The widget may show information, such as in a window or text box, and/orinclude buttons or other controls that allow the customer to accesscertain functionalities such as sharing or opening a file. In someimplementations, a widget is a common looking user interface componenthaving a portable portion of code that can be installed and executedwithin a separate web-based page without compilation. Some componentscan include corresponding and/or additional user interfaces and canaccess a variety of resources such as local resources (e.g., a calendar,contact information, etc. on the customer device) and/or remote networkresources (e.g., instant messaging, electronic mail, social networkingupdates, etc.).

In addition, embodiments of the contact center 200 may be configured tomanage deferrable interactions or activities (also referenced simply asdeferrable activities) and the routing thereof to human agents forcompletion. As should be understood, deferrable activities includeback-office work that can be performed off-line, examples of whichinclude responding to emails, letters, attending training, and otheractivities that do not entail real-time communication with a customer.To do this, the interaction (iXn) server 244 is configured to interactwith the routing server 218 for selecting an appropriate agent to handleeach of the deferable activities. Once assigned to a particular agent,the deferable activity is pushed to that agent, for example, appearingon the agent device 230 of the selected agent. As an example, thedeferable activity appear in a workbin 232 as a task for the selectedagent to complete. The functionality of the workbin 232 may beimplemented via any conventional data structure, such as, for example, alinked list, array, etc. Each of the agent devices 230 may include aworkbin 232, thus, workbins 232A, 232B, and 232C may be maintained inthe agent devices 230A, 230B, and 230C, respectively. As an example, aworkbin 232 may be maintained in the buffer memory of the correspondingagent device 230.

According to an embodiment, the contact center 200 may include auniversal contact server (UCS) 246, which is configured to retrieveinformation stored in the customer database 222 and direct informationfor storage therein. For example, the UCS 246 may be utilized as part ofthe chat feature to facilitate maintaining a history on how well chatsfor a particular customer were handled, which then may be used as areference for future chat communications. The UCS 246 also may beconfigured to facilitate maintaining a history of customers' preferencesregarding media channels, such as instances in which chat communicationsare acceptable and instances in which customers prefer alternate mediachannels. Additionally, the UCS 246 may be configured to record aninteraction history for each customer, capturing and storing dataregarding comments from agents, customer communication history, and thelike. Each of these data types may be stored on the customer database222 or on other modules as described functionality requires.

Example embodiments of the contact center 200 may further include areporting server 248 configured to generate reports from data aggregatedby the statistics server 226. Such reports may include near real-timereports or historical reports concerning the state of resources, suchas, for example, average wait time, abandonment rate, agent occupancy,etc. The reports may be generated automatically or in response tospecific requests from a requestor (e.g., agent, administrator, contactcenter application, etc.).

According to an embodiment, the media services 249 may provide audioand/or video services to support contact center features such as promptsfor an IVR or IMR system (e.g., playback of audio files), hold music,voicemails/single party recordings, multi-party recordings (e.g., ofaudio and/or video calls), speech recognition, dual tone multi frequency(DTMF) recognition, faxes, audio and video transcoding, secure real-timetransport protocol (SRTP), audio conferencing, video conferencing,coaching (e.g., support for a coach to listen in on an interactionbetween a customer and an agent and for the coach to provide comments tothe agent without the customer hearing the comments), call analysis, andkeyword spotting.

According to an embodiment, the analytics module 250 may provide systemsand methods for performing analytics on interaction data from aplurality of different data sources such as different applicationsassociated with a contact center or an organization. Aspects ofembodiments of the present invention are also directed to generating,updating, training, and modifying predictors or models 252 based oncollected interaction data. The models 252 may include behavior modelsof customers or agents. The behavior models may be used to predictbehaviors of, for example, customers or agents, in a variety ofsituations, thereby allowing embodiments of the present invention totailor interactions based on the predictions or to allocate resources inpreparation for predicted characteristics of future interactions, andthereby improving overall performance, including improving the customerexperience. It will be appreciated that, while the analytics module 250is depicted as being part of a contact center, such behavior models maybe implemented on customer systems (or, as also used herein, on the“customer-side” of the interaction) and used for the benefit ofcustomers.

According to exemplary embodiments, the analytics module 250 may haveaccess to the data stored in the storage device 220, including thecustomer database 222 and agent database 223. The analytics module 250also may have access to the interaction database 224, which may storedata related to interactions and interaction content (e.g., transcriptsof the interactions and events detected therein), interaction metadata(e.g., customer identifier, agent identifier, medium of interaction,length of interaction, interaction start and end time, department,tagged categories), and the application setting (e.g., the interactionpath through the contact center). As discussed more below, the analyticmodule 250 may be further configured to retrieve data stored within thestorage device 220 for use in developing and training algorithms andmodels 252, for example, by applying machine learning techniques.

One or more of the models 252 may be configured to predict customer oragent behavior and/or aspects related to contact center operation andperformance. Further, one or more of the models 252 may be used innatural language processing and, for example, include intent recognitionand the like. The models 252 may be developed based upon 1) known firstprinciple equations describing a system, 2) data, resulting in anempirical model, or 3) a combination of known first principle equationsand data. In developing a model for use with present embodiments,because first principles equations are not available or easily derived,it is generally preferred to build an empirical model based uponcollected and stored data. To properly capture the relationship betweenthe manipulated/disturbance variables and the controlled variables ofcomplex systems, the models 252 preferably are nonlinear. This isbecause nonlinear models can represent curved rather than straight-linerelationships between manipulated/disturbance variables and controlledvariables, which are common to complex systems such as those discussedherein. Given the foregoing requirements, a neural network-basedapproach is presently a preferred embodiment for implementing the models252. Neural networks, for example, may be developed based upon empiricaldata using advanced regression algorithms.

The analytics module 250 may further include an optimizer 254. As willbe appreciated, an optimizer may be used to minimize a “cost function”subject to a set of constraints, where the cost function is amathematical representation of desired objectives or system operation.As stated, the models 252 preferably are a non-linear model.Accordingly, the optimizer 254 may be a nonlinear programming optimizer.However, it is contemplated that the present invention may beimplemented by using, individually or in combination, a variety ofdifferent types of optimization approaches. These optimizationapproaches include, but not limited to, linear programming, quadraticprogramming, mixed integer non-linear programming, stochasticprogramming, global non-linear programming, genetic algorithms, andparticle/swarm techniques.

According to exemplary embodiments, the models 252 and the optimizer 254may together be used as an optimization system 255. For example, theanalytics module 250 may utilize the optimization system 255 as part ofan optimization process by which so aspect of contact center performanceand operational is enhanced or optimized, for example, aspects relatedto the customer experience, the agent experience, routing, function ofautomated processes, etc.

The various components, modules, and/or servers of FIG. 2 (as well asthe other figures included herein) may each include one or moreprocessors executing computer program instructions and interacting withother system components for performing the various functionalitiesdescribed herein. The computer program instructions may be stored in amemory implemented using a standard memory device, such as, for example,a random-access memory (RAM), or stored in other non-transitory computerreadable media such as, for example, a CD-ROM, flash drive, etc.Although the functionality of each of the servers is described as beingprovided by the particular server, a person of skill in the art shouldrecognize that the functionality of various servers may be combined orintegrated into a single server, or the functionality of a particularserver may be distributed across one or more other servers withoutdeparting from the scope of the present invention. Further, the terms“interaction” and “communication” are used interchangeably, andgenerally refer to any real-time and non-real-time interaction that usesany communication channel including, without limitation, telephone calls(PSTN or VoIP calls), emails, vmails, video, chat, screen-sharing, textmessages, social media messages, WebRTC calls, etc. Access to andcontrol of the components of the contact system 200 may be affectedthrough user interfaces (UIs) which may be generated on the customerdevices 205 and/or the agent devices 230. As noted above, the contactcenter 200 may operate as a hybrid system in which some or allcomponents are hosted remotely, such as in a cloud-based or cloudcomputing environment.

Chat Systems

Turning to FIGS. 3, 4 and 5, various aspects of chat features andsystems are discussed, as may be utilized in exemplary embodiments ofthe present invention. As will be seen, the present invention mayinclude or be enabled by a chat feature by which textual messages areexchanged between different parties, where those parties may includelive persons, such as customers and agents, as well as automatedprocesses, such as bots or chatbots. In general, a bot (also known as anInternet bot, is a software application that runs automated tasks orscripts over the Internet. Typically, bots perform tasks that are bothsimple and structurally repetitive, at a much higher rate than would bepossible for a human alone. A chatbot is a particular type of bot and,as used herein, is defined as a piece of software that conducts aconversation via auditory or textual methods. As will be appreciated,chatbots are often designed to convincingly simulate how a human wouldbehave as a conversational partner. Chatbots are typically used indialog systems for various practical purposes including customer serviceor information acquisition. Some chatbots use sophisticated naturallanguage processing systems, but many simpler ones scan for keywordswithin the input, then pull a reply with the most matching keywords, orthe most similar wording pattern, from a database. Chatbots can beclassified into usage categories such as conversational commerce(e-commerce via chat), analytics, communication, customer support,social, travel, etc.

The chat features and systems are presented generally with reference toexemplary embodiments of a chat server, chatbot, and chat interfaceillustrated, respectively, in FIGS. 3, 4, and 5. While these examplesare provided with respect to a chatbot implemented on the contact centerside, it should be understood that such a chatbot may be modified towardimplementation on the customer-side. Accordingly, as discussed morebelow, it should be appreciated that the exemplary chat systems of FIGS.3, 4, and 5 may be modified by one skilled in the art for analogous useon the customer-side of interactions with contact centers. It will beappreciated that, as provided herein, chatbots may be utilized by voicecommunications via converting text-to-speech and/or speech-to-text.

Referring specifically now to FIG. 3, a more detailed schematic blockdiagram is provided of the chat server 240 introduced in relation toFIG. 2. As stated above, FIG. 3 is provided for background purposes andas an exemplary module for implementing a chat feature. The chat server240 may be coupled to (e.g., in electronic communication with) acustomer device 205 operated by the customer over a data communicationsnetwork 210. The chat server 240 may be operated by a business orenterprise as part of a contact center 200 (e.g., FIG. 2) forimplementing and orchestrating aspects of chat conversations with thecustomers of the business, including both automated chats and chats withhuman agents. In regard to automated chats, the chat server 240 may hostone or more chat automation modules or chatbots 260A-260C (collectivelyreferenced as 260), which are configured with computer programinstructions for engaging in automated chat conversations. Thus,generally, the chat server 240 implements chat functionality, includingthe exchange of text-based or chat communications between a customerdevice 205 and an agent device 230 as well as between a customer device205 and a chatbot 260. As will be discussed more below, the chat server240 may include a customer interface module 265 and an agent interfacemodule 266 for generating particular UIs at the customer device 205 andthe agent device 230, respectively, that are included within the chatfunctionality.

The chatbots 260 may operate, for example, as an executable program thatcan be launched according to demand for the particular chatbot.According to an embodiment, the chat server 240 may operate as anexecution engine or environment for the chatbots 260, analogous toloading VoiceXML files to a media server for interactive voice response(IVR) functionality. Loading and unloading may be controlled by the chatserver 240, analogous to how a VoiceXML script may be controlled in thecontext of an interactive voice response. The chat server 240 mayprovide a means for capturing and collecting customer data in a unifiedway, similar to customer data capturing in the context of IVR. Such datacan be stored, shared, and utilized in a subsequent conversation,whether with the same chatbot, a different chatbot, an agent chat, oreven a different media type. According to an embodiment, the chat server240 is configured to orchestrate the sharing of data among the variouschatbots 260 as interactions are transferred or transitioned over fromone chatbot to another or from one chatbot to a human agent. Accordingto an embodiment, the data captured during interaction with a particularchatbot may be transferred along with a request to invoke a secondchatbot or human agent.

In exemplary embodiments, the number of chatbots 260 may vary accordingto the design and function of the chat server 240 and is not limited tothe number illustrated in FIG. 3. For example, different chatbots may becreated to have different profiles. The profile of a particular chatbotmay be used to select a chatbot with expertise for helping a customer ona particular subject control, for example, how the chatbot communicateswith a particular customer. Engaging chatbots with profiles that arecatered to specific types of customers may allow more effectivecommunication with such customers. For example, one chatbot may bedesigned or specialized to engage in a first topic of communication(e.g., opening a new account with the business), while another chatbotmay be designed or specialized to engage in a second topic ofcommunication (e.g., technical support for a product or service providedby the business), that is different from the first topic ofcommunication. In another example, the chatbots may be configured toutilize different dialects or slang or may have different personalitytraits or characteristics. For example, the vocabulary of the differentchatbots may be tailored to use the slang or diction of young people,elder people, people in a certain region of the country, and/or peoplehaving a certain language or ethnic background. The chat server 240 mayalso host a default chatbot that may be invoked at a beginning of a chatconversation if there is insufficient information about the customer toinvoke a more specialized chatbot. For example, if a customer intent isunknown when the conversation initially ensues, the default chatbot maybe invoked to ask questions about the customer intent. According to anembodiment, a chatbot may be customer selectable, for example, based onaccent, appearance, age group, language, etc., by way of a userinterface. Additionally, a chatbot may be assigned to a customer basedon demographic information of the customer. According to an embodiment,a chatbot profile may be selected based on information learned frompublicly available information (e.g., social media information) about acustomer.

According to an embodiment, a profile of a chatbot 260 may be stored ina profile database hosted in the storage device 220. The chatbot'sprofile data may include, without limitation, the chatbot's personality,gender, demographics, areas of expertise, and the like. According to anembodiment, for a given subject, including receptionist and conciergeservices, and specialists on particular products or services (e.g.,travel booking, opening accounts, etc.), there may be several differentchatbots 260, each with their own personality or profile.

Each of the different chatbots 260 may be configured, in conjunctionwith the chat server 240, to learn and evolve their behavior andresponses according to input by the customers. For example, in responseto customers reacting negatively to certain words, phrases, orresponses, the chatbots 260 may learn to use different words, phrases,or responses. Such learning may be supervised in order to preventundesired evolution of the personalities or profiles of the chatbots260. For example, changes to the personalities or profiles of thechatbots 260 may be first approved or validated by human supervisors,certain keywords or phrases may be identified or flagged, and customerfeedback may be analyzed. According to an embodiment, different chatbots260 may be configured to learn from each other, in addition to learningbased on customer feedback or agent feedback. For example, differentchatbots 260 may be configured to communicate and exchange data witheach other. In exemplary embodiments, the different chatbots 260 mayoperate as a neural network for deep learning and self-learningcapabilities, by exchanging data with one another.

As mentioned, the chat server 240 may include a customer interfacemodule 265 and an agent interface module 266. The customer interfacemodule 265 may be configured to generating user interfaces (UIs) fordisplay on the customer device 205 that facilitate chat communicationbetween the customer and the chatbots 260 and the customer and humanagents. The chat server 240 may include an agent interface module 266for generating particular UIs on the agent device 230 that facilitatechat communication between an agent operating an agent device 230 and acustomer operating a customer device 205. The agent interface module 266may also generate UIs on the agent device 230 that allow an agent tomonitor aspects of an ongoing chat between a chatbot 260 and a customer.The customer interface module 265 and the agent interface module 266,thus, may operate to facilitate the exchange of chat communicationsbetween the customer device 205 and one of the chatbots 260 and/or oneof the agent devices 230. For example, the customer interface module 265may transmit signals to the customer device 205 during a chat sessionthat are configured to generated particular UIs on the customer device205. As will be seen, those UIs generated on the customer device 205 mayinclude the text messages sent from chatbots 260 or human agents as wellas other non-text graphics that are intended to accompany the textmessages, such as, emoticons or animations, for display therewith.Likewise, the agent interface module 266 may transmit signals to theagent device 230 during a chat session that are configured to generatedparticular UIs on the agent device 230. As will be seen, those UIsgenerated on the agent device 230 may include the text messages sentfrom customer device 205. The UIs generated on the agent device 230 alsomay include an interface that facilitates the selection of non-textgraphics by the agent that are to accompany an outgoing text message tothe customer.

According to an embodiment, the chat server 240 may be implemented in alayered architecture, with a media layer, a media control layer, and thechatbots executed by way of the IMR server 216 (similar to executing aVoiceXML on an IVR media server).

As depicted in FIG. 2, the chat server 240 may further be configured tointeract with the knowledge management server 234 to query the serverfor knowledge information. The query, for example, may be based on aquestion received from the customer during a chat. Responses receivedfrom the knowledge management server 234 may then be provided to thecustomer as part of a chat response.

According to an embodiment, the chat server 240 may run on the samecomputer as the other servers of the contact center 200 depicted in FIG.2. The chat server 240 may also run on a separate computer equipped witha processor, which executes program instructions and interacts withother system components to perform various methods and operationsaccording to embodiments of the present invention. The chat server 240may also run on the cloud or serverless architecture. The chat server240 may include a memory, which operates as an addressable memory unitfor storing software instructions to be executed by the processor. Thememory may be implemented using any suitable memory device, such as arandom access memory (RAM), and may additionally operate as a computerreadable storage medium having non-transitory computer readableinstructions stored therein that, when executed by the processor, causethe processor to control and manage an automated chat communicationbetween the chat server 240, the customer device 205, and/or the agentdevice 230.

Referring specifically now to FIG. 4, a more detailed block diagram isprovided of an exemplary chat automation module or chatbot 260. Asstated, FIG. 4 is provided for background purposes and as an exemplaryimplementation of a chatbot. As would be understood by one of ordinaryskill in the art, aspects of chatbot 260 may be used or modified for usewith embodiments of the present invention. In the illustratedembodiment, the chatbot 260 includes a text analytics module 270, adialog manager 272, and an output generator 274. The text analyticsmodule is configured to analyze and understand natural language. In thisregard, the text analytics module may be configured with a lexicon ofthe language, a syntactic/semantic parser, and grammar rules forbreaking a phrase provided by the customer device 205, into an internalsyntactic and semantic representation. According to an embodiment, theconfiguration of the text analytics module depends on the particularprofile associated with the chatbot. For example, certain slang wordsmay be included in the lexicon for one chatbot but excluded from anotherchatbot.

In operation, the dialog manager 272 receives the syntactic and semanticrepresentation from the text analytics module 270 and manages thegeneral flow of the conversation based on a set of decision rules. Inthis regard, the dialog manager 272 maintains history and state of theconversation, and generates an outbound communication based on thehistory and state. The communication may follow the script of aparticular conversation path selected by the dialog manager 272. Asdescribed in further detail below, the conversation path may be selectedbased on an understanding of a particular purpose or topic of theconversation. The script for the conversation path may be generatedusing any of various languages and frameworks conventional in the art,such as, for example, artificial intelligence markup language (AIML),SCXML, or the like.

During the chat conversation, the dialog manager 272 selects a responsedeemed to be appropriate at the particular point of the conversationflow/script, and outputs the response to the output generator 274.According to an embodiment, the dialog manager 272 may also beconfigured to compute a confidence level for the selected response andprovide the confidence level to the agent device 230. According to anembodiment, every segment, step, or input in a chat communication mayhave a corresponding list of possible responses. Responses may becategorized based on topics (determined using a suitable text analyticsand topic detection scheme) and suggested next actions are assigned.Actions may include, for example, responses with answers, additionalquestions, assignment for a human agent to assist (e.g., bydisambiguating input from the customer), and the like. The confidencelevel may be utilized to assist the system with deciding whether thedetection, analysis, and response to the customer input is appropriateor sufficient, or whether a human agent should be involved. For example,a threshold confidence level may be assigned to invoke human agentintervention, based on one or more business rules. According to anembodiment, confidence level may be determined based on customerfeedback. For example, in response to detecting a negative reaction froma customer to an action or response taken by the chatbot, the confidencelevel may be reduced. Conversely, in response to detecting a positivereaction from a customer, the confidence level may be increased.

According to an embodiment, the response selected by the dialog manager272 may include information provided by the knowledge management server234. The information may be, for example, a link to a knowledge articlethat the chatbot may want to recommend to the customer in response to aquestion posed by the customer.

In exemplary embodiments, the output generator 274 takes the semanticrepresentation of the response provided by the dialog manager 272, mapsthe response to a chatbot profile or personality (e.g., by adjusting thelanguage of the response according to the dialect, vocabulary, orpersonality of the chatbot), and outputs an outbound text to bedisplayed at the customer device 205. The output text may beintentionally presented such that the customer interacting with achatbot is unaware that it is interacting with an automated process asopposed to a human agent. As will be seen, in accordance with otherembodiments, the output text may be linked with visual representations,such as emoticons or animations, integrated into the customer's userinterface.

Brief reference will now be made to FIG. 5, in which a webpage 280having an exemplary implementation of a chat feature 282 is presented.The webpage 280, for example, may be associated with a business orenterprise website and intended to initiate interaction betweenprospective or current customers visiting the webpage and a contactcenter associated with the enterprise. As will be appreciated, the chatfeature 282 may be generated on any type of customer device 205,including personal computing devices such as laptops, tablet devices, orsmart phones, to name a few non-limiting examples. Further, the chatfeature 282 may be generated as a window within a webpage or implementedas a full-screen interface. As in the example shown, the chat feature282 may be contained within a defined portion of the webpage 280 and,for example, may be implemented as a widget via the systems andcomponents described above and/or any other conventional means. As willbe appreciated, the chat feature 282 may include an exemplary way forcustomers to enter text messages for delivery to a contact centerassociated with a particular organization or enterprise.

As an example, the webpage 280 may be accessed by a customer via acustomer device, which provides a communication channel for interactingor chatting with bots or live agents. In exemplary embodiments, asshown, the chat feature 282 includes an interface generated on a screenof the customer device, such as customer device 205. This user interfaceof the chat feature 282 may be referred to herein as a customer chatinterface 284. The customer chat interface 284, for example, may begenerated by a customer interface module of a chat server, as alreadydescribed. The customer interface module may send signals to thecustomer device that are configured to generate a desired customer chatinterface 284 in accordance with the content of a chat message issued bya chat source, which as depicted is a chatbot named “Kate”. The customerchat interface 284 may be contained within a designated area or window,with that window covering a designated portion of the webpage 280. Thecustomer chat interface 284 also may be a text display area 286, whichis the area dedicated to the display of received and sent text messages.and a text input area 288, which facilitates the customer's input oftext messages. Though this may be achieved in other ways, the chatinterface of FIG. 5 illustrates one manner by which text messages may beentered by customers for communicating with an agent or chatbot of acontact center.

Before proceeding further with the description of the present invention,an explanatory note will be provided in regard to referencing systemcomponents—e.g., modules, servers, and other components—that havealready been introduced in the previous figures. Whether or not asubsequent reference includes the corresponding numerical identifiers ofFIGS. 1-5, it should be understood that the reference incorporates thepreviously discussed examples and, unless otherwise specificallylimited, may be implemented in accordance with those examples and/orusing other conventional technology capable of fulfilling the desiredfunctionality, as would be understood by one of ordinary skill in theart. Thus, for example, subsequent mention of a “contact center” shouldbe understood as referring to the exemplary “contact center 200” of FIG.2 and/or other conventional technology for implementing a contactcenter. As additional examples, a subsequent mention below to a“customer device”, “agent device”, “chat server”, “computing device”,“chatbot”, or “customer interface module” should be understood asreferring to the exemplary “customer device 205”, “agent device 230”,“chat server 240”, “computing device 200”, “chatbot 260”, or “customerinterface module 265”, respectively, of FIGS. 1-5, as well asconventional technology for fulfilling the same functionality.

Customer Automation Systems

Turning now to FIGS. 6 through 11, embodiments of the present inventioninclude systems and methods for automating and augmenting customeractions during various stages of interaction with a customer serviceprovider or contact center. As used herein, the various stages of suchinteractions may be classified as including pre-contact, during-contact,and post-contact stages (or pre-interaction, during-interaction, andpost-interaction stages).

With specifically reference now to FIG. 6, an exemplary customerautomation system 300 is shown which may be used in methods and systemsof the present invention. To better explain how the customer automationsystem 300 functions, reference will also be made to FIG. 7, which showsa flowchart 350 of an exemplary method for automating customer actionswhen interacting with contact centers. Additional information related tocustomer automation and related systems and methods are provided in U.S.application Ser. No. 16/151,362, filed on Oct. 4, 2018, entitled “Systemand Method for Customer Experience Automation”, the content of which isincorporated herein by reference. As will be seen, the customerautomation system 300 may be used as part of an automated personalassistant (or “personal bot”) 405, which is introduced in the discussionrelated to FIG. 8.

The customer automation system 300 of FIG. 6 represents a system thatmay be generally used for customer-side automations, which, as usedherein, refers to the automation of actions on behalf of a customer ininteractions with customer service providers or contact centers Suchinteractions may also be referred to as “customer-contact centerinteractions” or simply “customer interactions”. Further, in discussingsuch customer-contact center interactions, it should be appreciated thatreference to a “contact center” or “customer service provider” isintended to generally refer to any customer service department or otherservice provider associated with an organization or enterprise (such as,for example, a business, governmental agency, non-profit, school, etc.)with which a user or customer has business, transactions, affairs orother interests.

In exemplary embodiments, the customer automation system 300 may beimplemented as a software program or application running on a mobiledevice or other computing device, cloud computing devices (e.g.,computer servers connected to the customer device 205 over a network),or combinations thereof (e.g., some modules of the system areimplemented in the local application while other modules are implementedin the cloud. For the sake of convenience, embodiments of the presentinvention may be primarily described in the context of implementationvia an application running on the customer device 205. However, itshould be understood that present embodiments are not limited thereto.

The customer automation system 300 may include several components ormodules. For example, as shown, the customer automation system 300 mayinclude a user interface 305, a natural language processing (NLP) module310, an intent inference module 315, a script storage module 320, ascript processing module 325, a customer profile module 330, acommunication manager module 335, a text-to-speech module 340 a, aspeech-to-text module 340 b, and an application programming interface(API) 345, each of which will be described with more particularity withreference also to flowchart 350 of FIG. 7. It will be appreciated thatsome of the components of and functionalities associated with thecustomer automations system 300 may overlap with the chatbot systemsdescribed above in relation to FIGS. 3, 4, and 5. In cases where thecustomer automation system 300 and such chatbot systems are employedtogether as part of a customer-side implementation—such as in theexample of the personal bot 405 of FIG. 8—it is anticipated that suchoverlap may include the sharing of resources between the two systems.

In an example of operation, with specific reference now to the flowchart350 of FIG. 7, the customer automation system 300 receives input at aninitial step or operation 355. Such input may come from several sources.For example, a primary source of input may be the customer, where suchinput is received via the user interface 305 on the customer device(e.g., customer device 205). The input also may include data receivedfrom other parties, particularly parties interacting with the customerthrough the customer device. For example, information or communicationssent to the customer from the contact center may provide aspects of theinput. In either case, the input may be provided in the form of freespeech or text (e.g., unstructured, natural language input). Input alsomay include other forms of data received or stored on the customerdevice.

Continuing with the flow diagram 350, at an operation 360, the customerautomation system 300 parses the natural language of the input using theNLP module 310 and, therefrom, infers a customer intent using the intentinference module 315. That is, the customer's intent is determined giventhe input. For example, where the customer input is provided as speech,the speech may be transcribed into text by a speech-to-text system (suchas a large vocabulary continuous speech recognition or LVCSR system) aspart of the parsing by the NLP module 310. The transcription may beperformed locally on the customer device 205 or the speech may betransmitted over a network for conversion to text by a cloud-basedserver. In certain embodiments, for example, the intent inference module315 may automatically infer the customer's intent from the text of thecustomer input using artificial intelligence or machine learningtechniques. These artificial intelligence techniques may include, forexample, identifying one or more keywords from the customer input andsearching a database of potential intents corresponding to the givenkeywords. The database of potential intents and the keywordscorresponding to the intents may be automatically mined from acollection of historical interaction recordings. In cases where thecustomer automation system 300 fails to understand or completelyunderstand the intent from the customer's input, a selection of severalintents may be provided to the customer in the user interface 305. Thecustomer may then clarify his/her intent by selecting one of thealternatives or may request that other alternatives be provided.

After the customer's intent is determined, the flowchart 350 proceeds toan operation 365 where the customer automation system 300 loads a scriptassociated with the given intent. Such scripts, for example, may bestored and retrieved from the script storage module 320. As will beappreciated, the script may include a set of commands or operations,pre-written speech or text, and/or fields of parameters or data (also“data fields”), which represent data that is expected to be required inautomating an action for the customer. For example, the script mayinclude commands, text, and data fields that will be required tocomplete an interaction with a contact center in order to resolve theissue specified by the customer's intent. Scripts may be specific to aparticular contact center (or a particular organization) and, inexemplary embodiments, may be further tailored to resolving a particularissue. Scripts may be organized in a number of ways. In exemplaryembodiments, the scripts are organized in a hierarchical fashion, suchas where all scripts pertaining to a particular organization are derivedfrom a common “parent” script that defines common features. An exampleof common features might be common templates for authentication steps(e.g., account numbers and verification codes), where “child” scriptsinclude templates for the different types of issues to be resolved(e.g., double billing, requests for reductions in price, servicepausing, service plan modification, service cancellation, and the like).In exemplary embodiments, rather than a hierarchical relationship, thescripts are assembled from common tasks, such as combining“authentication” templates for authenticating with various serviceproviders and “issue” templates for resolving common issues that may beassociated with multiple providers.

The scripts may be produced via mining data, actions, and dialogue fromprevious customer interactions. Specifically, the sequences ofstatements made during a request for resolution of a particular issuemay be automatically mined from a collection of historical interactionsbetween customers and customer service providers. Systems and methodsmay be employed for automatically mining effective sequences ofstatements and comments, as described from the contact center agentside, are described in U.S. patent application Ser. No. 14/153,049“Computing Suggested Actions in Caller Agent Phone Calls By UsingReal-Time Speech Analytics and Real-Time Desktop Analytics,” filed inthe United States Patent and Trademark Office on Jan. 12, 2014, theentire disclosure of which is incorporated by reference herein.

With the script retrieved, the flowchart 350 proceeds to an operation370 where the customer automation system 300 processes or “loads” thescript. This action may be performed by the script processing module325, which performs it by filling in the data fields of the script withappropriate data pertaining to the customer. More specifically, thescript processing module 325 may extract customer data that is relevantto the anticipated interaction, with that relevance being predeterminedby the script selected as corresponding to the customer's intent.According to preferred embodiments, the data for some or most of thedata fields within the script may be automatically loaded with dataretrieved from customer data stored within the customer profile module330. As will be appreciated, the customer profile module 330 may storeparticular data related to the customer, for example, the customer'sname, birth date, address, account numbers, authentication information,and other types of information relevant to customer serviceinteractions. The data selected for storage within the customer profilemodule 330 may be based on data the customer has used in previousinteractions and/or include data values obtained directly by thecustomer. In case of any ambiguity regarding the data fields or missinginformation within a script, the script processing module 325 mayinclude functionality that prompts and allows the customer to manuallyinput the needed information.

Referring again to the flowchart 350, at an operation 375, the loadedscript may be transmitted to the customer service provider or contactcenter. As discussed more below, the loaded script may include commandsand customer data necessary to automate at least a part of aninteraction with the contact center on the customer's behalf. Inexemplary embodiments, the API 345 is used so to interact with thecontact center directly. Contact center may define a protocol for makingcommonplace requests to their systems, which is provided for in the API345. Such APIs may be implemented over a variety of standard protocolssuch as Simple Object Access Protocol (SOAP) using Extensible MarkupLanguage (XML), a Representational State Transfer (REST) API withmessages formatted using XML or JavaScript Object Notation (JSON), andthe like. Accordingly, the customer automation system 300 mayautomatically generate a formatted message in accordance with a definedprotocol for communication with a contact center, where the messagecontains the information specified by the script in appropriate portionsof the formatted message.

Personal Bot

With reference now to FIG. 8, an exemplary embodiment is provided of anautomated personal assistant or, as referenced herein, personal bot 405.As will be seen, the personal bot 405 is configured to automate aspectsof interactions with a customer service provider on behalf of acustomer. As stated above, present invention relates to systems andmethods for automating aspects of the customer-side of the interactionsbetween customers and customer service providers or contact centers.Accordingly, the personal bot 405 may provide ways to automate actionsthat customers are required to perform when contacting, interacting, orfollowing up with contact centers.

The personal bot 405, as used herein, may generally reference anycustomer-side implementation of any of the automated processes orautomation functionality described herein. Thus, it should be understoodthat, unless otherwise specifically limited, the personal bot 405 maygenerally employ any of the technologies discussed herein—includingthose related to the chatbots 260 and the customer automation system300—to enable or enhance automation services available to customers. Forexample, as indicated in FIG. 8, the personal bot 405 may include thefunctionality of the above-described customer automation system 300.Additionally, the personal bot 405 may include a customer-sideimplementation of a chatbot (for example, the chatbot 260 of FIGS. 4 and5), which will be referred herein as a customer chatbot 410. As will beseen, the customer chatbot 410 may be configured to interact privatelywith the customer in order to obtain feedback and direction from thecustomer pertaining to actions related to ongoing, future, or pastinteractions with contact centers. Further, the customer chatbot 410 maybe configured to interact with live agents or chatbots associated with acontact center on behalf of the customer.

As shown in FIG. 8, in regard to system architecture, the personal bot405 may be implemented as a software program or application running on amobile device or personal computing device (shown as a customer device205) of the customer (see “405A”). The personal bot 405 also may includeremote or cloud computing components (e.g., one or more computer serversconnected to the customer device 205 over a network 210), which may behosted in a cloud computing environment (or simply a “cloud”) 415 (see“405B”). For example, as shown in the illustrated example, elements ofthe script storage module 320 and the customer profile module 330 may bestored in databases in the cloud 415. It should be understood, however,that present embodiments are not limited to this arrangement and, forexample, may include other components being implemented in the cloud415.

Accordingly, as will be seen, embodiments of the present inventioninclude systems and methods for automatically initiating and conductingan interaction with a contact center to resolve an issue on behalf of acustomer. Toward this objective, the personal bot 405 may be configuredto automate particular aspects of interactions with a contact center onbehalf of the customer. Several examples of these types of embodimentswill now be discussed in which resources described herein—including thecustomer automation system 300 and customer chatbot 410—are used toprovide the necessary automation. In presenting these embodiments,reference is again made to previously incorporated U.S. application Ser.No. 16/151,362, entitled “System and Method for Customer ExperienceAutomation”, which includes further background and other supportingmaterials.

Pre-Interaction Automation

Embodiments of the present invention include the personal bot 405 andrelated resources automating one or more actions or processes by whichthe customer initiates a communication with a contact center forinteracting therewith. As will be seen, this type of automation isprimarily aimed at those actions normally occurring within thepre-contact or pre-interaction stage of customer interactions.

For example, in accordance with an exemplary embodiment, when a customerchooses to contact a contact center, the customer automation system 300may automate the process of connecting the customer with the contactcenter. For example, present embodiments may automatically navigate anIVR system of a contact center on behalf of the customer using a loadedscript. Further, the customer automation system 300 may automaticallynavigate an IVR menu system for a customer, including, for example,authenticating the customer by providing authentication information(e.g., entering a customer number through dual-tone multi-frequency orDTMF or “touch tone” signaling or through text to speech synthesis) andselecting menu options (e.g., using DTMF signaling or through text tospeech synthesis) to reach the proper department associated with theinferred intent from the customer's input. More specifically, thecustomer profile module 330 may include authentication information thatwould typically be requested of customers accessing customer supportsystems such as usernames, account identifying information, personalidentification information (e.g., a social security number), and/oranswers to security questions. As additional examples, the customerautomation system 300 may have access to text messages and/or emailmessages sent to the customer's account on the customer device 205 inorder to access one-time passwords sent to the customer, and/or may haveaccess to a one-time password (OTP) generator stored locally on thecustomer device 205. Accordingly, embodiments of the present inventionmay be capable of automatically authenticating the customer with thecontact center prior to an interaction.

In addition, the customer automation system 300 may facilitate acustomer contacting a contact center via multiple channels forcommunication, such as, call (e.g., voice and/or video), chat, ore-mail. In exemplary embodiments, the communication channels may includecalling, chatting, and leaving a message. Estimated wait times forinteractions with a live agent (e.g., call or chat) may also be shown tothe customer. For example, if the customer chooses to call and speakwith a live agent, the customer may be offered several options. Theseoptions might include to wait (e.g., “dial now and wait”), select acallback (e.g., “dial now and skip waiting”), or schedule a call for agiven time (e.g., “schedule a callback”). In exemplary embodiments, ifthe customer selects to schedule a call for a given time by opting for“schedule a callback,” for example, the customer automation system 300may access the customer's calendar (stored/accessible on the samecustomer device 205) and offer suggestions for free times in thecustomer's calendar. The customer automation system 300 may determinethat the customer is free at particular times over the next two days.These times may be automatically presented to the customer for selectionthereby. The customer may also choose to schedule the call at anothertime and input this into the user interface 305. Certain embodiments ofthe present invention may enable callback scheduling even when contactcenters do not directly support such a feature. For example, assumingthat the customer has scheduled a callback for 10:00 am, the system mayautomatically determine the approximate wait time during the timeperiods leading up to 10:00 am. This might be based on historical datacaptured from other customers contacting this particular organization orit may be based on wait time data published by the contact center. Thus,in accordance with exemplary embodiments, the customer automation system300 automatically connects to the contact center at a time prior to thescheduled call back time, based on the expected wait time, and suppliesthe set of information provided to the customer automation system 300 inaccordance with the script in order to be placed on hold by the contactcenter. For example, the customer automation system 300 mayautomatically determine that the expected wait time at 09:15 is 45minutes, and therefore initiates communication with the contact centerat 09:15 in order have an agent available to speak to the customer ataround 10:00. When the customer automation system 300 is connected to alive contact center agent (e.g., by detecting a ringing on the contactcenter end of the communication channel or by detecting a voice saying“hello”), an automatic notification may be sent to the customer (e.g.,by ringing at the customer device 205) and then the customer may beconnected to the live agent.

In accordance with other embodiments, the customer automation system 300may automate a process for preparing an agent before a call from acustomer. For example, the customer automation system 300 may send arequest that the agent study certain materials provided by the customerbefore the live call happens.

During-Interaction Automation

Embodiments of the present invention further include the personal bot405 and related resources automating the actual interaction (or aspectsthereof) between the customer and a contact center. As will be seen,this type of automation is primarily aimed at those actions normallyoccurring within the during-contact or during-interaction stage ofcustomer interactions.

For example, the customer automation system 300 may interact withentities within a contact center on behalf of the customer. Withoutlimitation, such entities may include automated processes, such aschatbots, and live agents. Once connected to the contact center, thecustomer automation system 300 may retrieve a script from the scriptstorage module 320 that includes an interaction script (e.g., a dialoguetree). The interaction script may generally consist of a template ofstatements for the customer automation system 300 to make to an entitywithin the contact center, for example, a live agent. In exemplaryembodiments, the customer chatbot 410 may interact with the live agenton the customer's behalf in accordance with the interaction script. Asalready described, the interaction script (or simply “script”) mayconsist of a template having defined dialogue (i.e., predetermined textor statements) and data fields. As previously described, to “load” thescript, information or data pertinent to the customer is determined andloaded into the appropriate data fields. Such pertinent data may beretrieved from the customer profile module 330 and/or derived from inputprovided by the customer through the customer interface 305. Accordingto certain embodiments, the customer chatbot 410 also may be used tointeract with the customer to prompt such input so that all of thenecessary data fields within the script are filled. In otherembodiments, the script processing module 325 may prompt the customer tosupply any missing information (e.g., information that is not availablefrom the customer profile module 330) to fill in blanks in the templatethrough the user interface 305 prior to initiating a communication withthe contact center. In certain embodiments, the script processing module325 may also request that the customer confirm the accuracy of all ofthe information that the customer automation system 300 will provide tothe contact center.

Once the loaded script is complete, for example, the interaction withthe live agent may begin with an initial statement explaining the reasonfor the call (e.g., “I am calling on behalf of your customer, Mr. ThomasAnderson, regarding what appears to be double billing.”), descriptionsof particular details related to the issue (e.g., “In the previous threemonths, his bill was approximately fifty dollars. However, his mostrecent bill was for one hundred dollars.”), and the like. While suchstatements may be provided in text to the contact center, it may also beprovided in voice, for example, when interacting with a live agent. Inregard to how such an embodiment may function, a speech synthesizer ortext-to-speech module 340 a may be used to generate speech to betransmitted to the contact center agent over a voice communicationchannel. Further, speech received from the agent of the contact centermay be converted to text by a speech-to-text converter 340 b, and theresulting text then may be processed by the customer automation system300 or customer chatbot 410 so that an appropriate response in thedialogue tree may be found. If the agent's response cannot be processedby the dialogue tree, the customer automation system 300 may ask theagent to rephrase the response or may connect the customer to the agentin order to complete the transaction.

While the customer automation system 300 is conducting the interactionwith the live agent in accordance with the interaction script, the agentmay indicate his or her readiness or desire to speak to the customer.For the agent, readiness might occur after reviewing all of the mediadocuments provided to the agent by the customer automation system 300and/or after reviewing the customer's records. In exemplary embodiments,the script processing module 325 may detect a phrase spoken by the agentto trigger the connection of the customer to the agent via thecommunication channel (e.g., by ringing the customer device 205 of thecustomer). Such triggering phrases may be converted to text by thespeech-to-text converter 340 b and the natural language processingmodule 310 then may determine the meaning of the converted text (e.g.,identifying keywords and/or matching the phrase to a particular clusterof phrases corresponding to a particular concept).

As another example, the customer automation system 300 may presentautomatically generated “quick actions” to the customer based on thecustomer's inferred intent and other data associated with the ongoinginteraction. In some circumstances, the “quick actions” require nofurther input from the customer. For example, the customer automationsystem 300 may suggest sending an automatically generated text or emailmessage to the contact center directly from a main menu screen, wherethe message describes the customer's issue. The message may be generatedautomatically by the script processing module based on a messagetemplate provided by the script, where portions of the template thatcontain customer-specific and incident-specific data are automaticallyfilled in based on data collected about the customer (e.g., from thecustomer profile) and that the customer has supplied (e.g., as part ofthe initial customer input). For example, in the case where the customerinput references a question about a possible double billing by aparticular service provider, the script processing module 325 canreference previous billing statements, which may be stored as part ofthe customer profile module 330, to look for historical charges. Thecustomer automation system 300 infers from these previous billingstatements that the amount charged for the period in question wasunusually high. In such cases, the system may automatically generate amessage which may contain the information about the customer's typicalbills and the problem with the current bill. The customer can direct thecustomer automation system 300 to send the automatically generatedmessage directly to the contact center associated with the serviceprovider. In exemplary embodiments, the script may provide multipletemplates, and the customer may select from among the templates and/oredit a message prior to sending, in order to match the customer'spersonality or preferred tone of voice.

In other exemplary embodiments, the personal bot 405 may automateprocesses that augment a current or ongoing interaction between thecustomer and a contact center (e.g., between the customer and either achatbot or a live agent of the contact center). While the personal bot405 may not handle the interaction in such embodiments, the personal botmay work behind the scenes to facilitate the customer's interaction witha contact center, so to increase the likelihood of a desirable outcomefor the customer. In such embodiments, once the interaction has beeninitiated with a live agent, meta-data regarding the interaction may bedisplayed to the customer in the user interface 305. This may be donethroughout the interaction, with the information being update based onthe progression of the ongoing interaction. Examples of such informationmight include, but not be limited to, name of the contact center, nameof the department reached, reason for the call, name of the contactcenter agent, name of other agents who were on the call, etc. Accordingto exemplary embodiments, this type of information may include atranscript of the ongoing call so that the customer can easily look backat previous statements. In addition, the customer automation system 300may display other types of information to the customer that is foundpertinent given, for example, the recognition of certain key wordswithin the transcript of the ongoing conversation. That is, the customerautomation system 300 may push relevant content from a knowledge base(e.g., the knowledge system 238 of FIG. 2) to the customer given thecontent of the transcript of the interaction.

The customer automation system 300 also may enable the customer andagent to share relevant content with each other throughout theinteraction. For example, in one embodiment, the agent or customer mayshare screens, documents (contracts, warranties, etc.), photos, andother files with each other. Other files may also be shared, such asscreenshots of content captured by one of the parties during theconversation, a current view from a camera, links, photographs of brokenor failed products, screenshots of error messages, copies of documents,proofs of purchase, or any other supporting file. The customerautomation system 300, thus, may provide functionality that facilitatesthe customer supplying or sharing additional or augmenting material toan agent of the contact center that is relevant to an ongoinginteraction. To do this, for example, a supplemental communicationchannels (e.g., a data channel) is established in parallel to theprimary communication channel (e.g., a voice communication channel or atext communication channel) to transfer the augmenting informationbetween the customer and the contact center agent. In certainembodiments, these documents may be provided along with an automaticallygenerated “quick actions” message. For example, such quick actionmessages may prompt the customer to take a photo of the broken part, forinclusion in the shared material.

In accordance with other embodiments, the communication manager 335monitors conditions for a customer based on specified intents andautomatically generates notifications to be presented to the customerthrough the user interface 305. For example, based the previous activityof the customer (for example, the customer's billing statements, whichmay be stored in the customer profile module 330, and communicationsfrom different contact centers), the communication manager 335 mayautomatically generate notifications which might be of interest to thecustomer. Examples of a notification generated by the communicationmanager may include a reminder about the upcoming expiration of a deal,an offer of a new deal, actions for the customer, and the like. Forexample, the notification may offer quick actions that can be performeddirectly from the notification screen, such as how to get a specificdeal, call a contact center about a specific deal, search for moredeals, cancel a service, etc. The communication manager 335 maycustomize notifications to the customer based on the customer's previousdeals, billing statements, crowdsourced information about how similarcustomers reacted to deals, personal preferences, and the like. Thecommunication manager 335 may provide such functionality through theuser interface 305 for the customer to search for more deals based ontheir needs. Should the customer select this option, the customerautomation system 300 may present some relevant deals that areidentified from a database of deals.

In accordance with other embodiments, the customer automation system 300may provide ‘end of deal’ notifications. In such cases, the customer isinformed about the ending of deal, contract, business arrangement, orthe like. For example, a customer may be notified about the ending of aninternet package deal with their current internet service provider(ISP). The customer may be presented with the best deals offered bytheir current ISP and the best deals offered by other ISPs. Continuingwith this example, the customer automation system 300 may offer specificdeals without requiring communication with the contact center, such as acall-in to the relevant customer service department. Pricing may also beshown along with other comparisons relevant to the customer. Forexample, promotional offers may be compared to the average usage of thecustomer (e.g., based on the customer profile) and current pricing oftheir plan. Other suggested options that are specific to the customerintent in the notification may also be presented, such as a “cancelservice” option and an option to “search more deals.” Should thecustomer select the “cancel service” option, the customer automationsystem 300 may send a cancellation request to the contact centerautomatically. The customer automation system 300 may also search formore deals which fit the customer's needs and present these whether thecustomer has selected to cancel their service or just search foradditional deals. These may also be presented to the customer.

According to other embodiments, the customer automation system 300 maymonitor statements made by the contact center agent and automaticallyoffers guidance to the customer in real-time. For example, the customerautomation system 300 converts the contact center agent's speech to textusing the speech-to-text converter 340 b and processes the text usingthe natural language processing module 310. In exemplary embodiments,the natural language processing module 310 detects when the agent ismaking an offer and compares it to a database of other offers made byagents of the organization that the customer is speaking with. Thisdatabase of other offers is crowdsourced from other customers. Afteridentifying a corresponding matching offer in the database, the rankingof the offer compared to other offers is identified in order todetermine whether the agent could make a better offer.

According to still other embodiments, the customer automation system 300may present information to the customer about prior interactions with aparticular contact center or organization. For example, such informationmay be retrieved during an ongoing interaction to show the current agentwhat other agents have said.

Customer Privacy Automation

Embodiments of the present invention further include the personal bot405 and related resources functioning to automate aspects related toprivacy for a customer. More particularly, the customer automationsystem 300 of the personal bot 405 may allow customers to manage privacyor data sharing with organizations and corresponding contact centers.

In accordance with exemplary embodiments, for example, the customerautomation system 300 may facilitate the customer managing settings forprivacy and data sharing (or simply “data sharing settings”) globally,for example, across all providers and data types. The customer isenabled to manage data sharing settings on a per-organization basis bychoosing which data type to share with each specific organization. Asanother example, the customer is enabled to manage data (e.g., datawithin a customer profile) according to data type. In such cases, thecustomer may choose which organization or which types of organizationsto share each particular data type. In more detail, each field of datain the customer profile may be associated with at least one permissionsetting (e.g., in exemplary embodiments, each field of data may have adifferent permission setting for each provider). Further, userinterfaces may be provided through the customer device 205 that allowthe customer to adjusting data sharing settings and/or permissionsettings. Within such user interfaces, data sharing settings orpermission settings may be made adjustable on a per data type, perorganization basis, per type of organization basis, etc.

In accordance with exemplary embodiments, the customer automation system300 may offer a plurality of levels for data sharing settings orpermission settings. For example, in one embodiment, three differentlevels of permission settings are offered: share data, share anonymousdata, and do not share any data. Anonymous data may include, forexample, genericized information about the customer such as gender, zipcode of residence, salary band, etc. Some aspects of embodiments of thepresent invention may enable compliance with the General Data ProtectionRegulation (GDPR) of the European Union (EU). In other embodiments, thecustomer automation system 300 provides functionality for a customer toexercise the “right to be forgotten” with all organizations (e.g.,providers and/or business) that the customer has interacted with. Inother embodiments, the customer can switch on/off the sharing of each ofthe data types. When selecting a specific data type, the customer canselect to send this data in an anonymized form to the provider or todelete the previously shared data with a particular organization.Additionally, the customer can delete all data types that werepreviously shared with an organization, for example, by clicking on the‘trash’ button provided in the customer interface. According to oneembodiment of the present invention, the deletion of the data mayinclude the customer automation system 300 loading an appropriate scriptfrom the script storage module 320 in order to generate a formal requestto the associated organization to delete the specified data. As notedabove, for example, the customer automation system 300 may be used tomake such request by initiating a communication with a live agent of theorganization or by accessing an application programming interfaceprovided by the organization.

Post-Interaction Automation

Embodiments of the present invention include methods and systems foridentifying outstanding matters or pending actions for a customer thatneed additional attention or follow-up, where those pending actions wereraised during an interaction between the customer and a contact center.Once identified, other embodiments of the present invention includemethods and systems for automating follow-up actions on behalf of thecustomer for moving such pending actions toward a resolution. Forexample, via the automation resources disclosed herein, the personal bot405 may automate subsequent or follow-up actions on behalf of acustomer, where those follow-up actions relate to actions pending from aprevious interaction with a customer service provider. As will beappreciated, this type of automation is primarily aimed at those actionsnormally occurring within the post-contact or post-interaction stage ofa customer interaction, however it also includes the automation ofaction that also can be characterized as preceding or prompting asubsequent customer interaction.

With reference to FIGS. 9, 10 and 11, an exemplary interaction isillustrated of a conversation transcript between a customer and acontact center agent stemming from a call made by the customer todiscuss his internet connection. Using this text, exemplaryfunctionality is provided as to how the interaction may be processed bythe personal bot 405 in order to identify unresolved or pending actionsand/or take follow-up actions for the customer in relation thereto.

With specific reference to FIG. 9, text of the conversation between thecustomer and the agent is presented, which may be referred to as aninteraction 505. It will be appreciated that, while the example includesthe customer interacting with a live agent, the present functionalitycould also be used in situations in which the customer instead interactswith an automated process or chatbot. In the dialogue of the interaction505, the customer is contacting the contact center to discuss a slowinternet connection that he has been experiencing. The conversationprogresses with the agent diagnosing the reason for the slowconnection—the customer using his current plan's allotment of high-speeddata—and then offering the customer an upgraded data plan that includesunlimited high-speed data. The agent tells the customer that the upgradeoffer includes special discounts and free equipment if the customeragrees to enroll for a specified term. Toward the end of the interaction505, the agent discusses with the customer several actions and timeperiods relevant to enrolling the customer in the upgraded plan and howlong it will take for the customer to again have a high-speed dataconnection. The customer then agrees to enroll in the upgraded plan asoffered by the agent.

It will be appreciated that, as the interaction 505 concludes, bothparties to the conversation have agreed upon or suggested (or, as alsoused herein, “promised”) a course of action that includes severalactions being performed in the future. (Note: these actions, as alreadymentioned, will be referenced herein as “pending actions” and,generally, described as being “promised” by one of the parties in theinteraction. It should be understood that the use of the term “promised”is intended broadly and may be used herein to described instances wherea pending action is merely implied or recommended in a party'sstatements, as it may be desirable in certain embodiments to identify apending action for follow-up where no express or binding promise isactually made.) For example, several actions have been promised by theagent (and, by extension, the business or organization the agentrepresents) that need to be fulfilled so that the customer, in fact, isenrolled in the upgraded plan and the high-speed data connection isdelivered to him in the manner promised. As will now be discussed,embodiments of the present invention may effectuate through customerautomation the performance of these pending actions.

In accordance with exemplary embodiments, once an interaction hasconcluded, example embodiments of the present invention (for example,via functionality associated with the personal bot 405 and/or customerautomation system 300) may record and document the interaction for acustomer, e.g., storing data related thereto in the customer profilemodule 330. For example, different aspects related to the communicationmay be recorded and stored, including text, voice, and/or video. Thecustomer automation system 300 may further document the interaction bystoring and indexing any messages, documents, files, and other mediainvolved or shared during in the interaction and, thereby, provide acustomer with the ability to later search this material. As an example,a customer may search for keywords spoken by an agent in order toretrieve saved audio that was spoken near the keywords. As anotherexample, after conducting a technical support call, a customer may havethe ability to recall and view an image that was shared and annotated byan agent when explaining how to set up a particular piece of equipment.A customer may then be able to view specific details of an interaction,such as the timing of particular spoken lines in the conversation andwhat files were being shared at the time.

In relation to the example interaction 505 of FIG. 9, the personal bot405 may save the conversation as a transcript or recording. If saved asa recording, the recording may later be transformed to text viavoice-to-text transcription. In any case, an interaction transcript ofthe interaction 505 may be created and saved within a database, wherethat database is accessible to the personal bot 405. As will now bediscussed, the personal bot 405 may include analysis tools that, whenapplied to the interaction transcript, facilitate customer-sideautomation.

With specific reference to FIG. 10, the results of a first type ofanalysis are presented. In this case, the personal bot has analyzed thetranscript from the interaction 505 and, from that analysis, recognized,inferred, and/or classified an overall “context” of the interaction andidentified “intents” (i.e., the meaning or intention behind spokenphrases or word groupings), which, as will be seen, then may be used toidentify pending actions. In performing this analysis, any of themethods and systems disclosed herein may be used, including the use ofconventional technologies, as would occur to one of ordinary skill inthe art. For example, the personal bot may have a machine learning orartificial intelligence (AI) engine that is trained with predefined setof intents that are segregated by context. Thus, the personal bot mayfirst determine a reason or context of the customer query, i.e., thecontext. Upon determining the context, the analysis may proceed withsegmenting and classifying the interaction transcript in accordance witha predefined set of intents that correspond to that particular context.The results of this step in the analysis are presented in FIG. 10 in apersonal bot classifications 510 section, which is provided as anannotation to the interaction transcript 505.

More specifically, in relation to the example conversation of theinteraction 505, the analysis of the personal bot may begin bydetermining a context 515, which often is found in an initial segment ofthe exchange in a customer's response to an agent asking the reason forthe call. A trained model may be used to do this. For example, atraining data set (e.g., a data set including data pertaining to priorinteractions between customers and contact centers) may be used to trainan NLP algorithm or model—which also may be referred to as a “contextrecognizer model”— and, once trained, the model may be used to recognizethe context of the interaction 505. In the example provided, because thecustomer has called to express his frustration with his internetconnection being slow, the analysis determines that the context 515 is a“slow internet connection”.

As a next step of the analysis, the personal bot may retrieve apredefined list of intents that corresponds to the context, i.e., “slowinternet connection”. In accordance with the list of intents, thepersonal bot may segment the conversation and classify the segments bythose intents. That is, the analysis continues with personal botchunking or segmenting the conversation based on, for example, keywordsand topics covered within particular portions or sections of theexchange. As shown, one of the segments is the one in which the context515 of the interaction is found, with the other segments being primarilydevoted to different topics within the context 515. Based on the topicor subtopics covered in each of these other segments, the personal botthen may classify each with an intent 520 selected from the predefinedlist. As an example, a trained model may be used to do this. Forexample, a training data set (e.g., a data set including data pertainingto prior interactions between customers and contact centers) may be usedto train an NLP algorithm or model—which also may be referred to as a“intents recognizer model”—and, once trained, the model may be used torecognize an intent for each of the other segments. As shown in theexample of FIG. 10, the intents 520 inferred for the remaining segmentsare referred to as “exhausted high-speed bandwidth”, “selling upgrade topremium plan”, and lastly, “customer accepts premium plan”.

These intents 520 then may be used to identify any unresolved,outstanding, or, as referred to herein, pending actions 525. As usedtherein, a “pending action” is defined as any action that is agreedupon, promised, or otherwise suggested by one of the parties during aninteraction, where the action is to be performed or completed after theinteraction has ended. In regard to the relationship between intents andpending actions, an intent may refer to a broader action or objective,while a pending action within that intent may refer to specific actionsnecessary to make that broader objective happen. Thus, as will bediscussed more below, one of the intents identified in FIG. 10 refers acustomer's desire to enroll in a new upgraded plan. To make this intenthappen, several actions must be taken, some of which may be taken careof during the interaction, with others requiring action afterinteraction has concluded. These actions—which have to be completedafter the interaction is concluded—are the ones that present embodimentsmay classify as pending actions 525.

As will be appreciated, certain pending actions may regularly appear inrelation to certain intents. According to example embodiments, someintents may have a predefined list of pending actions. Like thepredefined list of intents that correspond to a particular context, thepredefined list of pending actions may result in more accuraterecognition of pending actions when a particular intent is identified.

Within an interaction, pending actions may be found in statements madeby either the customer or agent, which may include chatbotrepresentatives in place of each, and may make either party responsiblefor completing the action. It should be appreciated that, in the case ofan agent, a pending action may be identified for actions that will becompleted by other representatives or agents (i.e., not just the agenthimself). That is, a pending action may be identified when the agentsuggests or promises an action that will be handled by anotherrepresentative of the contact center and/or enterprise, business, ororganization associated with the contact center. For example, astatement by an agent saying that the agent will call the customer backat a later time creates an identifiable pending action, which is afuture act performed by the agent of calling the customer back. Asanother example, a statement by an agent promising a service call by atechnician on an upcoming day creates an identifiable pending action,which is the future act performed by the technician of completing theservice call. As mentioned, customer statements can also create pendingactions. For example, if the customer suggests that he will do somethingin the future, such as tells an agent that he will forward certaindocuments to the agent later in the day, this creates an identifiablepending action for the customer.

Returning to the specific example of FIG. 10, the results of theanalysis demonstrate that the exemplary embodiments does not identifyany pending actions 525 in the “exhausted high-speed bandwidth” intentsegment. Similarly, none is identified in the “selling upgrade topremium plan” intent segment. As will be appreciated, these resultsgenerally stem from the fact that, in each case, neither the customernor agent make statements that can be reasonably construed as suggestingor promising the completion of a specified action in the future.However, as will now be discussed, present embodiments may be configuredto identify several such pending actions 525 in the “customer acceptspremium plan” intent segment.

As illustrated, a first identified pending action 525 is referenced as“provide premium plan.” As will be appreciated, with the customeraccepting the upgrade to the premium plan, the agent's statements aboutwhat comes with the premium plan substantially creates a promise toactually provide those services. Such promised future actions may beclassified as pending actions.

As further illustrated, a second identified pending action 525 isreferenced as “provide free equipment”. In this case, the agent makesstatements regarding one or more actions that will be taken after theinteraction is completed in order to provide the customer with certainfree equipment (i.e., a modem and router). These statements are madecontingent on the customer upgrading to the premium plan. Once thecustomer agrees to the upgrade, the agent's statements effectivelybecome a promise, and the future actions related to providing theequipment becomes classified as a pending action.

A third identified pending action 525 is referenced as “restorehigh-speed connection”. In this case, the agent makes statementsregarding one or more future actions that will be taken in regard torestoring the customer's high-speed connection once the customer agreesto the premium plan upgrade. When the customer accepts the upgrade, theagent's statements effectively become a promise, and the future actionsrequired to restore the customer's high-speed connection is classifiedas a pending action.

A fourth such pending action 525 is referenced as “stay enrolled inpremium plan”. In this case, the pending action is on the customer-sideof the interaction. That is, once the customer has accepted the upgrade,he has agreed to provide payment for the services, as necessary, overthe term of the agreement. Thus, once the customer accepts the upgrade,the future action of paying for those services and staying enrolledbecomes classified as a pending action.

With reference now to FIG. 11, with several pending actions 525identified, embodiments of the present invention may proceed withidentifying corresponding target timeframes 530. As used herein, atarget timeframe 530 is a time period or deadline associated with theperformance or fulfilment of the pending action 525 to which itcorresponds. As discussed more below, once identified, a targettimeframe 530 may be used to orchestrate the timing of automatedfollow-up actions by the personal bot on behalf of the customer.

The analysis for identifying a target timeframe may include naturallanguage processing via a trained model or neural network, which mayinclude key word or phrase spotting, particularly within the portion ofthe transcript in which the corresponding pending action 525 isidentified. In example embodiments, however, the present invention mayinclude a target timeframe recognizer model that recognizes relevanttimeframe language associated or used in conjunction with the pendingactions 525. For example, the interaction transcript may be analyzed bya target timeframe recognizer model, with the model outputting a targettimeframe 525 for each previously identified pending action 525. Inaccordance with an exemplary embodiment, a training data set (e.g., adata set including data pertaining to prior interactions betweencustomers and contact centers) is used to train the target timeframerecognizer model.

The present system and methods used to identify target timeframes may beconfigured to recognize several different types, allowing for thenecessary flexible for consistent use across a variety of situations. Aswill now be discussed, these categories may include: a) definite targettimeframes; b) deducible target timeframes; and c) indefinite targettimeframes.

In regard to the category of definite target timeframes, thisclassification includes those timeframes that are stated in theinteraction transcript a straightforward or direct or non-ambiguous way.Examples of these types of target timeframes are found in the followingstatements:

-   -   “Your request will be processed within 24 hours.”    -   “Your payment will be credited in the next 6 hours.”    -   “All our technical experts are currently busy, but we can assign        someone to call you back within the next 2 hours.”    -   “We are still working on the problem and will probably get back        to you in another hour.”    -   “It will take us about 2 hours to verify your documents.”    -   “Your complaint is already filed, and it will be resolve it in        the next 2 business days.”

In each of these examples, a distinct and specific timeframe ismentioned in relation to the performance of some action in the future.System and methods of the present invention may be trained to recognizeand decipher these types such timeframes so that a target timeframe isassigned to each identified pending action.

In regard to the next category, deducible target timeframes, as usedherein, are those timeframes that become clear and distinct with someadditional information. In such cases, the pending action may be clearstated, but the target timeframe is not expressed in a clear ornumerical way, as in the example above. That is, some further learnedintelligence must be applied in these cases to determine an appropriatetarget timeframe for the pending action. Examples of these types oftimeframes are found in the following statements:

-   -   “Please check back during business hours.”    -   “I can check if there are better deals for you during        Thanksgiving holidays.”    -   “Christmas week is the right time for you to check back        regarding this.”    -   “The issue needs one of our skilled technical experts. You can        expect a positive update after the holidays.”        In these examples, while the exact timeframe may not be stated        directly, it may be deduced if the input text is syntactically        handled correctly and/or additional information is acquired. For        example, in the first example, the personal bot may need to        search and find the relevant “business hours” information and,        once this is known, the target timeframe for calling back can be        known. In the next three examples, the personal bot may pinpoint        a specific target timeframe once the dates for the referenced        holidays are known.

In regard to the category of indefinite timeframes, as used herein,these are those timeframes that are defined in accordance with lessdefinitive or vague language. That is, statements made in theinteraction reference to a timeframe, however the language used todescribe that timeframe is open to some interpretation. Examples ofthese types of are found in the following statements:

-   -   “The item will be on stock in few days.”    -   “This whole week is crazy, but just give me more time and I will        get it done.”    -   “The product is currently not in stock. You should call us back        later.”    -   “I will check with my manager and get back to you soon.”        In regard to these examples, the target timeframe recognizer        model may be trained to infer an approximate timeframe or        deadline for completing a pending action given an analysis of        the interaction script. In training the model, the system may        analyze similar contexts to predict appropriate ranges for such        indefinite timeframes. Such models may be further improved when        input is received from the customer to confirm assumptions made        by the model. That is, the customer may be asked to confirm the        timing of a follow-up action by the personal bot. The customer        may choose to modify the deduced timeframe. The modified        timeframe may then be used to update the target timeframe        recognizer model so that the model learns and adepts. This may        be done on a per customer basis or applied more globally. In        ambiguous cases, the target timeframe for a pending action may        be confirmed via a prompt and question to the customer via the        customer device.

Returning to the specific example of FIG. 11, the analysis results inidentifying target timeframes 530 for each of the pending actions 525.Thus, in regard to the first pending action 530—entitled “providepremium plan”—the target timeframe 520 is identified as “2 YEARS”. Thistwo-year period reflects the time the internet provider has promised toprovide the customer with unlimited high-speed data per the premiumplan. In regard to the second pending action 530—entitled “provide freeequipment”—the target timeframe 530 is identified as “2 BUSINESS DAYS”.This period reflects the time in which the agent promised delivery andinstallation of the free modem and router. In regard to the thirdpending action 530—entitled “restore high-speed connection”—the targettimeframe 530 is identified as “20 MINUTES”. This period reflects thetime the agent stated it would take for the customer to again have ahigh-speed connection if he signed up for the premium plan. And,finally, in regard to the fourth pending action 530—entitled “stayenrolled in premium plan”—the target timeframe 530 is identified as “2YEARS”. This period reflects the time that the customer promised to stayenrolled in the premium plan.

According the exemplary embodiments, the personal bot 405 may use theidentified pending actions and target timeframes to automate theperformance of follow-up actions on behalf of the customer. Thefollow-up actions may be actions intended or anticipated to help move apending action toward a resolution or completion. In some embodiments,the personal bot may develop a follow-up workflow in which one or morefollow-up actions are scheduled in relation to the target timeframe.

For example, in accordance with present embodiments, the follow-upaction may take the form of a reminder, notification, inquiry, or offerto help made to the customer pertaining to a promise made to thecustomer by the agent during a previous interaction. As an example, thepersonal bot may provide a voice or text message to the customerstating: “John, Walmart said it would be calling you back todayregarding the return request of your previous order.”

The personal bot may present this type of reminder with other automatedfollow-up actions that the customer can then decide to use. First, thepersonal bot may offer to automate the process of connecting to thecustomer to the enterprise, for example, “John, do you want me to startthis interaction with Walmart for you now?” Second, the personal bot,when possible, may offer to handle the entire interaction for thecustomer, for example, “John, would you like me to handle thisinteraction with Walmart for you?” Third, the personal bot may simplyprovide a link with the initial question that, when activate by thecustomer, calls or otherwise contacts the enterprise to begin aninteraction that the customer then handles, for example, “Activate thefollowing link [CHAT LINK] to begin a chat session with Walmart” or“Activate the following link [PHONE NUMBER LINK] to place a call toWalmart.”

As another example, in accordance with present embodiments, thefollow-up action may take the form of a reminder, notification, inquiry,or offer to help made to the customer pertaining promises made by thecustomer to an agent during a previous interaction. Thus, when thecustomer has mentioned an action for himself in an interaction, thepersonal bot can help by later reminding him of this. For example, thepersonal bot may provide a voice or text message to the customerstating: “John, you told American Express you would get back to themtoday in regard to your expired credit card.” As another example, “John,you needed to send your address proofs to Y bank within 24 hours. Shallwe send it now? Or should I remind you later?” As with the above, thepersonal bot may present this type of reminder with offers relating toother automated follow-up actions that the customer can then decide touse.

Now, with specific reference again to FIG. 11, when this type offunctionality is applied to the example interaction 505, several typesof notifications and offers to help may be communicated to the customer.

For example, after 20 minutes has passed since the end of theinteraction 505, the personal bot may remind the customer that hishigh-speed connection should be restored and then inquire whether thishas happened yet. If it has not happened, other follow-up actionsfacilitating another or subsequent interaction with the contact centermay be suggested and then performed by the personal bot once permissionto do so is received from the customer to do so.

As another example, after two business days have passed since the end ofthe interaction 505, the personal bot may remind the customer that hisfree equipment should have been delivered and installed and then inquireas to whether this has taken place yet. If it has not happened, otherfollow-up actions facilitating another or subsequent interaction withthe contact center may be suggested and then performed by the personalbot once permission to do so is received from the customer.

As another example, near the end of the two-year enrollment term for thepremium plan, the personal bot may remind the customer that the end ofthe term is nearing and inquire as to any actions the customer may wantto take, e.g., provide notice terminating the plan at the end of thespecified term, consider other service options that the personal botcould present to the customer, etc. On the other side, the personal bot,when appropriate, may remind the customer about his obligations underthe premium plan once he accepted it. These may include remindersregarding the due date of the monthly fee or, if the customer beganresearching a new internet provider, remind him of the term remaining onhis current premium plan and/or advise him as to any applicable fees orpenalties for early termination. In this way, the follow-up actions ofthe present invention may serve to both remind the customer of pendingactions from previous interactions and facilitate the fulfilment of theunderlying promised actions.

As another example, present invention may include a computer-implementedmethod for automating actions for a customer in relation to aninteraction between the customer and an agent of a contact center,wherein the interaction may include an exchange of statements made bythe customer and the agent. The method may include the steps of:receiving at least a transcript of the interaction; via a firstanalysis, analyzing the transcript of the interaction; from results ofthe first analysis, identifying: a pending action, wherein the pendingaction comprises an action promised by the customer or the agent thatwill be resolved after the interaction; and a target timeframe forresolving the pending action; given the pending action, determining afollow-up workflow that may include one or more follow-up actions, eachof the one or more follow-up actions including an action intended toassist the customer to resolve the pending action; and automaticallyexecuting the one or more follow-up actions.

According to exemplary embodiments, the follow-up workflow may include aworkflow schedule including one or more predetermined times forautomatically executing respective ones of the one or more follow-upactions. The one or more predetermined times may be determined inrelation to the target timeframe of the pending action.

According to exemplary embodiments, the one or more follow-up actionsmay include a first follow-up action. According to exemplaryembodiments, the first follow-up action may include generating a firstuser interface on a display of the customer device, wherein the firstuser interface may be configured to communicate a notification relatedto the pending action to the customer.

According to exemplary embodiments, the pending action may include anaction promised by the agent, and the notification may communicate areminder to the customer of the action promised by the agent.

According to exemplary embodiments, the pending action may include anaction promised by the customer, and the notification may communicate areminder to the customer of the action promised by the customer, and thenotification may communicate a reminder to the customer of the pendingaction.

According to exemplary embodiments, the notification may include a linkto a relevant portion of the transcript of the interaction in which thepending action may be discussed. According to exemplary embodiments, thenotification may communicate to the customer a period of time remainingin the target timeframe given a current time.

According to exemplary embodiments, the one or more predetermined timesmay include a first predetermined time that corresponds to the firstfollow-up action. The first predetermined time may be configured tocoincide with an expiration of the target timeframe.

According to exemplary embodiments, the one or more follow-up actionsmay include a second follow-up action. The second follow-up action mayinclude an action facilitating a subsequent interaction between thecustomer and the contact center. According to exemplary embodiments, thesecond follow-up action may include automating the process by which thecustomer connects with the contact center to initiate the subsequentinteraction. According to exemplary embodiments, the second follow-upaction may include providing a link that, upon activating, is configuredto open a communication channel with the contact center to initiate thesubsequent interaction. According to exemplary embodiments, the secondfollow-up action may include navigating an automated system of thecontact center for establishing contact therewith. According toexemplary embodiments, the second follow-up action may includecontacting the contact center and performing the subsequent interactionon behalf the customer.

According to exemplary embodiments, the performing the subsequentinteraction on behalf of the customer may include posing an inquirypertaining to the pending action to one of the one or more agents of thecontact center. According to exemplary embodiments, the performing thesubsequent interaction on behalf of the customer further may includereceiving an answer to the inquiry from the one of the one or moreagents. The method may further include the step of delivering the answerto the customer. According to exemplary embodiments, the performing thesubsequent interaction on behalf of the customer may include deliveringa document pertaining to the pending action to the contact center.According to exemplary embodiments, the performing the subsequentinteraction on behalf of the customer may include requesting andreceiving a document pertaining to the pending action from the contactcenter.

According to exemplary embodiments, the first analysis may include usingone or more natural language processing neural networks to: determine acontext of the transcript; classify a segment of the transcript with anintent selected from a predefined list of intents that corresponds tothe context; given the intent, identify the pending action in thesegment of the transcript; and determine a target timeframe for thepending action. In regard to the determining the target timeframe, theone or more natural language processing neural networks may be trainedto identify three classifications of target timeframes, includingdefinite target timeframes, deducible target timeframes, and indefinitetarget timeframes. According to exemplary embodiments, the one or morenatural language processing neural networks may be trained by feedbackfrom the customer in regard to the indefinite target timeframes.

As one of skill in the art will appreciate, the many varying featuresand configurations described above in relation to the several exemplaryembodiments may be further selectively applied to form the otherpossible embodiments of the present invention. For the sake of brevityand taking into account the abilities of one of ordinary skill in theart, each of the possible iterations is not provided or discussed indetail, though all combinations and possible embodiments embraced by theseveral claims below or otherwise are intended to be part of the instantapplication. In addition, from the above description of severalexemplary embodiments of the invention, those skilled in the art willperceive improvements, changes and modifications. Such improvements,changes and modifications within the skill of the art are also intendedto be covered by the appended claims. Further, it should be apparentthat the foregoing relates only to the described embodiments of thepresent application and that numerous changes and modifications may bemade herein without departing from the spirit and scope of the presentapplication as defined by the following claims and the equivalentsthereof.

That which is claimed:
 1. A computer-implemented method for automatingactions for a customer in relation to an interaction between thecustomer and an agent of a contact center, wherein the interactioncomprises an exchange of statements made by the customer and statementsmade by the agent, the method comprising: receiving at least atranscript of the interaction; via a first analysis, analyzing thetranscript of the interaction; from results of the first analysis,identifying: a pending action, wherein the pending action comprises anaction promised by the customer or the agent that will be resolved afterthe interaction; and a target timeframe for resolving the pendingaction; given the pending action, determining a follow-up workflow thatincludes one or more follow-up actions, each of the one or morefollow-up actions comprising an action intended to assist the customerto resolve the pending action; and automatically executing the one ormore follow-up actions.
 2. The method according to claim 1, wherein thefollow-up workflow includes a workflow schedule comprising one or morepredetermined times for automatically executing respective ones of theone or more follow-up actions, the one or more predetermined times beingdetermined in relation to the target timeframe of the pending action;wherein the one or more follow-up actions comprises a first follow-upaction that includes generating a first user interface on a display ofthe customer device, the first user interface being configured tocommunicate a notification related to the pending action to thecustomer.
 3. The method according to claim 2, wherein the pending actioncomprises an action promised by the agent; and wherein the notificationcommunicates a reminder to the customer of the action promised by theagent.
 4. The method according to claim 2, wherein the pending actioncomprises an action promised by the customer; and wherein thenotification communicates a reminder to the customer of the actionpromised by the customer.
 5. The method according to claim 2, whereinthe notification communicates a reminder to the customer of the pendingaction; and wherein the notification communicates to the customer aperiod of time remaining in the target timeframe given a current time.6. The method according to claim 5, wherein the notification comprises alink to a relevant portion of the transcript of the interaction in whichthe pending action is discussed; and wherein: the one or morepredetermined times comprises a first predetermined time thatcorresponds to the first follow-up action; and the first predeterminedtime is configured to coincide with an expiration of the targettimeframe.
 7. The method according to claim 5, wherein the one or morefollow-up actions comprises a second follow-up action; and wherein thesecond follow-up action comprises an action facilitating a subsequentinteraction between the customer and the contact center.
 8. The methodaccording to claim 7, wherein the second follow-up action comprisesautomating the process by which the customer connects with the contactcenter to initiate the subsequent interaction, wherein the secondfollow-up action includes providing a link that, upon activating, isconfigured to open a communication channel with the contact center toinitiate the subsequent interaction.
 9. The method according to claim 7,wherein the second follow-up action comprises automating the process bywhich the customer connects with the contact center to initiate thesubsequent interaction, wherein the second follow-up action includesnavigating an automated system of the contact center for establishingcontact therewith.
 10. The method according to claim 7, wherein thesecond follow-up action comprises contacting the contact center andperforming the subsequent interaction on behalf the customer.
 11. Themethod according to claim 10, wherein the contact center has one or moreagents; and wherein the performing the subsequent interaction on behalfof the customer comprises posing an inquiry pertaining to the pendingaction to one of the one or more agents of the contact center.
 12. Themethod according to claim 11, wherein the performing the subsequentinteraction on behalf of the customer further comprises receiving ananswer to the inquiry from the one of the one or more agents; furthercomprising the step of delivering the answer to the customer.
 13. Themethod according to claim 10, wherein the contact center has one or moreagents; and wherein the performing the subsequent interaction on behalfof the customer comprises delivering a document pertaining to thepending action to the contact center.
 14. The method according to claim10, wherein the contact center has one or more agents; and wherein theperforming the subsequent interaction on behalf of the customercomprises requesting and receiving a document pertaining to the pendingaction from the contact center.
 15. The method according to claim 5,wherein the first analysis comprises using one or more natural languageprocessing neural networks to: determine a context of the transcript;classify a segment of the transcript with an intent selected from apredefined list of intents that corresponds to the context; given theintent, identify the pending action in the segment of the transcript;and determine a target timeframe for the pending action.
 16. The methodaccording to claim 15, wherein, in regard to the determining the targettimeframe, the one or more natural language processing neural networksis trained to identify three classifications of target timeframes,including definite target timeframes, deducible target timeframes, andindefinite target timeframes; and wherein the one or more naturallanguage processing neural networks is trained by feedback from thecustomer in regard to the indefinite target timeframes.
 17. A system forautomating actions for a customer in relation to an interaction betweenthe customer and an agent of a contact center, wherein the interactioncomprises an exchange of statements made by the customer and statementsmade by the agent, the system comprising: a hardware processor; and amachine-readable storage medium on which is stored instructions thatcause the hardware processor to execute a process, wherein the processcomprises the steps of: receiving at least a transcript of theinteraction; via a first analysis, analyzing the transcript of theinteraction; from results of the first analysis, identifying: a pendingaction, wherein the pending action comprises an action promised by thecustomer or the agent that will be resolved after the interaction; and atarget timeframe for resolving the pending action; given the pendingaction, determining a follow-up workflow that includes one or morefollow-up actions, each of the one or more follow-up actions comprisingan action intended to assist the customer to resolve the pending action;and automatically executing the one or more follow-up actions.
 18. Thesystem according to claim 17, wherein the follow-up workflow includes aworkflow schedule comprising one or more predetermined times forautomatically executing respective ones of the one or more follow-upactions, the one or more predetermined times being determined inrelation to the target timeframe of the pending action; and wherein theone or more follow-up actions comprises a first follow-up action thatincludes generating a first user interface on a display of the customerdevice, the first user interface being configured to communicate anotification related to the pending action to the customer.
 19. Thesystem according to claim 18, wherein the pending action comprises anaction promised by the agent; and wherein the notification communicatesa reminder to the customer of the action promised by the agent.
 20. Thesystem according to claim 18, wherein the pending action comprises anaction promised by the customer; and wherein the notificationcommunicates a reminder to the customer of the action promised by thecustomer.
 21. The system according to claim 18, wherein the notificationcommunicates a reminder to the customer of the pending action; andwherein the notification communicates to the customer a period of timeremaining in the target timeframe given a current time.
 22. The systemaccording to claim 21, wherein the one or more follow-up actionscomprises a second follow-up action; and wherein the second follow-upaction comprises an action facilitating a subsequent interaction betweenthe customer and the contact center.
 23. The system according to claim22, wherein the second follow-up action comprises automating the processby which the customer connects with the contact center to initiate thesubsequent interaction.
 24. The system according to claim 22, whereinthe second follow-up action comprises contacting the contact center andperforming the subsequent interaction on behalf the customer.
 25. Thesystem according to claim 24, wherein the contact center has one or moreagents; and wherein the performing the subsequent interaction on behalfof the customer comprises: posing an inquiry pertaining to the pendingaction to one of the one or more agents of the contact center; andreceiving an answer to the inquiry from the one of the one or moreagents; further comprising the step of delivering the answer to thecustomer.
 26. The system according to claim 24, wherein the contactcenter has one or more agents; and wherein the performing the subsequentinteraction on behalf of the customer comprises requesting and receivinga document pertaining to the pending action from the contact center. 27.The system according to claim 21, wherein the first analysis comprisesusing one or more natural language processing neural networks to:determine a context of the transcript; classify a segment of thetranscript with an intent selected from a predefined list of intentsthat corresponds to the context; given the intent, identify the pendingaction in the segment of the transcript; and determine a targettimeframe for the pending action.